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Merge branch 'master' into rmExtraExo

time-shift
Houtan Bastani 2015-05-18 17:16:55 +02:00
parent 524528bbde 2fe7a4156d
commit e389ab90bf
113 changed files with 5892 additions and 2164 deletions
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1043
doc/dynare.texi
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File diff suppressed because it is too large Load Diff

13
license.txt
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@ -53,6 +53,19 @@ Copyright: 2001-2012 Nikolaus Hansen
2012 Dynare Team
License: GPL-3+
Files: matlab/optimization/solvopt.m
Copyright: 1997-2008 Alexei Kuntsevich and Franz Kappel
2008-2015 Giovanni Lombardo
2015 Dynare Team
License: GPL-3+
Files: matlab/optimization/simulated_annealing.m
Copyright: 1995 E.G.Tsionas
1995-2002 Thomas Werner
2002-2015 Giovanni Lombardo
2015 Dynare Team
License: GPL-3+
Files: matlab/endogenous_prior.m
Copyright: 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
2013 Dynare Team

8
matlab/McMCDiagnostics.m
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@ -86,14 +86,14 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for geweke_interval. Using the default of [0.2 0.5].\n')
options_.convergence.geweke.geweke_interval=[0.2 0.5];
end
first_obs_begin_sample = max(1,ceil(options_.mh_drop*options_.mh_replic));
last_obs_begin_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(1)*options_.mh_replic*(1-options_.mh_drop));
first_obs_end_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(2)*options_.mh_replic*(1-options_.mh_drop));
first_obs_begin_sample = max(1,ceil(options_.mh_drop*NumberOfDraws));
last_obs_begin_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(1)*NumberOfDraws*(1-options_.mh_drop));
first_obs_end_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(2)*NumberOfDraws*(1-options_.mh_drop));
param_name=[];
for jj=1:npar
param_name = strvcat(param_name,get_the_name(jj,options_.TeX,M_,estim_params_,options_));
end
fprintf('\nGeweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d.\n',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,options_.mh_replic);
fprintf('\nGeweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d.\n',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,NumberOfDraws);
fprintf('p-values are for Chi2-test for equality of means.\n');
Geweke_header={'Parameter', 'Post. Mean', 'Post. Std', 'p-val No Taper'};
print_string=['%',num2str(size(param_name,2)+3),'s \t %12.3f \t %12.3f \t %12.3f'];

24
matlab/PosteriorIRF.m
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@ -16,7 +16,7 @@ function PosteriorIRF(type)
% functions associated with it(the _core1 and _core2).
% See also the comments random_walk_metropolis_hastings.m funtion.
% Copyright (C) 2006-2013 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -93,8 +93,15 @@ elseif strcmpi(type,'gsa')
else
MhDirectoryName = CheckPath('prior',M_.dname);
end
delete([MhDirectoryName filesep M_.fname '_IRF_DSGEs*.mat']);
delete([MhDirectoryName filesep M_.fname '_IRF_BVARDSGEs*.mat']);
%delete old stale files before creating new ones
delete_stale_file([MhDirectoryName filesep M_.fname '_IRF_DSGEs*.mat']);
delete_stale_file([MhDirectoryName filesep M_.fname '_IRF_BVARDSGEs*.mat']);
delete_stale_file([MhDirectoryName filesep M_.fname '_irf_dsge*.mat']);
delete_stale_file([MhDirectoryName filesep M_.fname '_irf_bvardsge*.mat']);
delete_stale_file([MhDirectoryName filesep M_.fname '_param_irf*.mat']);
if strcmpi(type,'posterior')
B = options_.sub_draws;
options_.B = B;
@ -112,16 +119,7 @@ else% type = 'prior'
B = options_.prior_draws;
options_.B = B;
end
try
delete([MhDirectoryName filesep M_.fname '_irf_dsge*.mat'])
catch
disp('No _IRFs (dsge) files to be deleted!')
end
try
delete([MhDirectoryName filesep M_.fname '_irf_bvardsge*.mat'])
catch
disp('No _IRFs (bvar-dsge) files to be deleted!')
end
irun = 0;
IRUN = 0;
irun2 = 0;

49
matlab/PosteriorIRF_core2.m
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@ -1,17 +1,28 @@
function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam,ThisMatlab)
% function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% Generates the Posterior IRFs plot from the IRFs generated in
% PosteriorIRF_core1
%
% PARALLEL CONTEXT
% Perform in parallel execution a portion of the PosteriorIRF.m code.
% See also the comment in random_walk_metropolis_hastings_core.m funtion.
% Performs in parallel execution a portion of the PosteriorIRF.m code.
% For more information, see the comment in random_walk_metropolis_hastings_core.m
% function.
%
% INPUTS
% See the comment in random_walk_metropolis_hastings_core.m funtion.
% o myimput [struc] The mandatory variables for local/remote
% parallel computing obtained from random_walk_metropolis_hastings.m
% function.
% o fblck and nblck [integer] The Metropolis-Hastings chains.
% o whoiam [integer] In concurrent programming a modality to refer to the differents thread running in parallel is needed.
% The integer whoaim is the integer that
% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
% cluster.
% o ThisMatlab [integer] Allows us to distinguish between the
% 'main' matlab, the slave matlab worker, local matlab, remote matlab,
% ... Then it is the index number of this slave machine in the cluster.
%
% OUTPUTS
% o myoutput [struc]
% Contained:
% OutputFileName (i.e. the figures without the file .txt).
% o myoutput [struc] Contained: OutputFileName (i.e. the figures without the file .txt).
%
% ALGORITHM
% Portion of PosteriorIRF.m function code. Specifically the last 'for' cycle.
@ -19,7 +30,7 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% SPECIAL REQUIREMENTS.
% None.
%
% Copyright (C) 2006-2013 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -100,13 +111,9 @@ for i=fpar:npar,
set(0,'CurrentFigure',hh)
subplot(nn,nn,subplotnum);
if ~MAX_nirfs_dsgevar
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
set(h1,'FaceColor',[.9 .9 .9]);
set(h1,'BaseValue',min(HPDIRF(:,1,j,i)));
h1 = area(1:options_.irf,HPDIRF(:,2,j,i),'FaceColor',[.9 .9 .9],'BaseValue',min(HPDIRF(:,1,j,i))); %grey below HPDIsup and minimum of HPDIinf
hold on
h2 = area(1:options_.irf,HPDIRF(:,1,j,i),'FaceColor',[1 1 1],'BaseValue',min(HPDIRF(:,1,j,i)));
set(h2,'FaceColor',[1 1 1]);
set(h2,'BaseValue',min(HPDIRF(:,1,j,i)));
h2 = area(1:options_.irf,HPDIRF(:,1,j,i),'FaceColor',[1 1 1],'BaseValue',min(HPDIRF(:,1,j,i))); %white below HPDIinf and minimum of HPDIinf
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
box on
@ -114,18 +121,14 @@ for i=fpar:npar,
xlim([1 options_.irf]);
hold off
else
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
set(h1,'FaceColor',[.9 .9 .9]);
set(h1,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
hold on;
h2 = area(1:options_.irf,HPDIRF(:,1,j,i));
set(h2,'FaceColor',[1 1 1]);
set(h2,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
h1 = area(1:options_.irf,HPDIRF(:,2,j,i),'FaceColor',[.9 .9 .9],'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))])); %grey below HPDIsup and minimum of HPDIinf and HPDIRFdsgevar
hold on
h2 = area(1:options_.irf,HPDIRF(:,1,j,i),'FaceColor',[1 1 1],'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))])); %white below HPDIinf and minimum of HPDIinf and HPDIRFdsgevar
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
plot(1:options_.irf,MeanIRFdsgevar(:,j,i),'--k','linewidth',2)
plot(1:options_.irf,HPDIRFdsgevar(:,1,j,i),'--k','linewidth',1)
plot(1:options_.irf,HPDIRFdsgevar(:,2,j,i),'--k','linewidth',1)
% plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
box on
axis tight
xlim([1 options_.irf]);
@ -156,6 +159,4 @@ for i=fpar:npar,
end
end% loop over exo_var
myoutput.OutputFileName = OutputFileName;

2
matlab/add_path_to_mex_files.m
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@ -29,7 +29,7 @@ if isoctave
else
% Add win32 specific paths for Dynare Windows package
if strcmp(computer, 'PCWIN')
tmp = [dynareroot '../mex/matlab/win32-7.5-8.4/'];
tmp = [dynareroot '../mex/matlab/win32-7.5-8.5/'];
if exist(tmp, 'dir')
mexpath = tmp;
if modifypath

15
matlab/bfgsi1.m
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@ -1,13 +1,20 @@
function H = bfgsi1(H0,dg,dx)
% H = bfgsi1(H0,dg,dx)
% dg is previous change in gradient; dx is previous change in x;
% Update Inverse Hessian matrix
%
% Inputs:
% H0 [npar by npar] initial inverse Hessian matrix
% dg [npar by 1] previous change in gradient
% dx [npar by 1] previous change in x;
%
% 6/8/93 version that updates inverse hessian instead of hessian
% itself.
%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/bfgsi.m
%
% Copyright (C) 1993-2009 Christopher Sims
% Copyright (C) 2009-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -41,4 +48,4 @@ else
disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
H=H0;
end
save H.dat H
save('H.dat','H')

39
matlab/check_valid_ver.m Normal file
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@ -0,0 +1,39 @@
function check_valid_ver(ver)
%function check_valid_ver(ver)
% Checks that ver is valid
%
% INPUTS
% ver [string] dynare version number
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
test_ver = strsplit(ver, {'.', '-'});
errmsg = 'check_valid_ver: the desired version must be in the proper format';
assert (length(test_ver) == 3 && ...
~isempty(str2double(test_ver{1})) && ...
~isempty(str2double(test_ver{2})), errmsg);
if isnan(str2double(test_ver{3}))
assert(strcmp(test_ver{3}, 'unstable'), errmsg);
end
end

12
matlab/cli/prior.m
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@ -28,6 +28,18 @@ function varargout = prior(varargin)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if isempty(varargin) || ( isequal(length(varargin), 1) && isequal(varargin{1},'help'))
skipline()
disp('Possible options are:')
disp(' + table Prints a table describing the priors.')
disp(' + moments Computes and displays moments of the endogenous variables at the prior mode.')
disp(' + optimize Optimizes the prior density (starting from a random initial guess).')
disp(' + simulate Computes the effective prior mass (using a Monte-Carlo).')
disp(' + plot Plots the marginal prior densities.')
skipline()
return
end
global options_ M_ estim_params_ bayestopt_ oo_ objective_function_penalty_base
donesomething = false;

22
matlab/conditional_variance_decomposition_mc_analysis.m
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@ -1,7 +1,27 @@
function oo_ = ...
conditional_variance_decomposition_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, var_list, endogenous_variable_index, mh_conf_sig, oo_)
% This function analyses the (posterior or prior) distribution of the
% endogenous conditional variance decomposition.
% endogenous variables' conditional variance decomposition.
%
% INPUTS
% NumberOfSimulations [integer] scalar, number of simulations.
% type [string] 'prior' or 'posterior'
% dname [string] directory name where to save
% fname [string] name of the mod-file
% Steps [integers] horizons at which to conduct decomposition
% exonames [string] (n_exo*char_length) character array with names of exogenous variables
% exo [string] name of current exogenous
% variable
% var_list [string] (n_endo*char_length) character array with name
% of endogenous variables
% endogenous_variable_index [integer] index of the current
% endogenous variable
% mh_conf_sig [double] 2 by 1 vector with upper
% and lower bound of HPD intervals
% oo_ [structure] Dynare structure where the results are saved.
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
% Copyright (C) 2009-2013 Dynare Team
%

47
matlab/convert_oo_.m Normal file
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@ -0,0 +1,47 @@
function oo_ = convert_oo_(oo_, ver)
%function oo_ = convert_oo_(oo_, ver)
% Converts oo_ from oo_.dynare_version to ver
%
% INPUTS
% oo_ [struct] dynare output struct
% ver [string] desired oo_ output version
%
% OUTPUTS
% oo_ [struct] dynare output struct
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
check_valid_ver(ver);
if isfield(oo_, 'dynare_version')
ver_orig = oo_.dynare_version;
else
ver_orig = '4.4.3';
end
if strcmp(ver_orig, ver)
return;
end
if ver_less_than(ver_orig, '4.5.0') && ver_greater_than_equal(ver, '4.5.0')
oo_.exo_simul = oo_.exo_simul';
end
end

20
matlab/covariance_mc_analysis.m
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@ -1,8 +1,24 @@
function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables covariance matrix.
% endogenous variables' covariance matrix.
%
% INPUTS
% NumberOfSimulations [integer] scalar, number of simulations.
% type [string] 'prior' or 'posterior'
% dname [string] directory name where to save
% fname [string] name of the mod-file
% vartan [char] array of characters (with nvar rows).
% nvar [integer] nvar is the number of stationary variables.
% var1 [string] name of the first variable
% var2 [string] name of the second variable
% mh_conf_sig [double] 2 by 1 vector with upper
% and lower bound of HPD intervals
% oo_ [structure] Dynare structure where the results are saved.
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
% Copyright (C) 2008-2013 Dynare Team
% Copyright (C) 2008-2015 Dynare Team
%
% This file is part of Dynare.
%

117
matlab/disp_dr.m
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@ -1,5 +1,5 @@
function disp_dr(dr,order,var_list)
% function disp_dr(dr,order,var_list)
% Display the decision rules
%
% INPUTS
@ -7,8 +7,8 @@ function disp_dr(dr,order,var_list)
% order [int]: order of approximation
% var_list [char array]: list of endogenous variables for which the
% decision rules should be printed
% Copyright (C) 2001-2012 Dynare Team
%
% Copyright (C) 2001-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -55,44 +55,67 @@ for i=1:nvar
end
end
% get length of display strings
header_label_length=16; %default
for ii=1:length(ivar)
header_label_length=max(header_label_length,length(deblank(M_.endo_names(k1(ivar(ii)),:)))+2);
end
header_label_format = sprintf('%%%ds',header_label_length);
value_format_float = sprintf('%%%d.6f',header_label_length);
value_format_zero = sprintf('%%%dd',header_label_length);
% account for additional characters introduced by auxiliary variables
if ~isempty(M_.aux_vars)
aux_vars_type = [M_.aux_vars.type];
if any(aux_vars_type==4)
aux_var_additional_characters=14;
else
aux_var_additional_characters=3;
end
else
aux_var_additional_characters=0;
end
var_name_width=max([max(size(deblank(M_.endo_names(k1(ivar),:)),2)),max(size(deblank(M_.exo_names),2))]);
%deal with covariances
if order > 1
var_name_width=max(2*(var_name_width+aux_var_additional_characters)+2,20); %account for covariances, separated by comma
else
var_name_width=max(var_name_width+aux_var_additional_characters,20);
end
label_format = sprintf('%%-%ds',var_name_width);
%% start displayimg
disp('POLICY AND TRANSITION FUNCTIONS')
% variable names
str = ' ';
str = char(32*ones(1,var_name_width));
for i=1:nvar
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
str = [str sprintf(header_label_format,deblank(M_.endo_names(k1(ivar(i)),:)))];
end
disp(str);
%
% constant
%
str = 'Constant ';
str=sprintf(label_format,'Constant');
flag = 0;
for i=1:nvar
x = dr.ys(k1(ivar(i)));
if order > 1
x = x + dr.ghs2(ivar(i))/2;
end
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
end
if order > 1
str = '(correction) ';
str = sprintf(label_format,'(correction)');
flag = 0;
for i=1:nvar
x = dr.ghs2(ivar(i))/2;
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -108,15 +131,10 @@ for k=1:nx
else
str1 = subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2);
end
str = sprintf('%-20s',str1);
str = sprintf(label_format,str1);
for i=1:nvar
x = dr.ghx(ivar(i),k);
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -127,15 +145,10 @@ end
%
for k=1:nu
flag = 0;
str = sprintf('%-20s',M_.exo_names(k,:));
str = sprintf(label_format,M_.exo_names(k,:));
for i=1:nvar
x = dr.ghu(ivar(i),k);
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -149,19 +162,14 @@ if order > 1
flag = 0;
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
subst_auxvar(k1(klag(j,1)),klag(j,2)-M_.maximum_lag-2));
str = sprintf('%-20s',str1);
str = sprintf(label_format,str1);
for i=1:nvar
if k == j
x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
else
x = dr.ghxx(ivar(i),(k-1)*nx+j);
end
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -174,19 +182,14 @@ if order > 1
for k = 1:nu
for j = 1:k
flag = 0;
str = sprintf('%-20s',[M_.exo_names(k,:) ',' M_.exo_names(j,:)] );
str = sprintf(label_format,[deblank(M_.exo_names(k,:)) ',' deblank(M_.exo_names(j,:))] );
for i=1:nvar
if k == j
x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
else
x = dr.ghuu(ivar(i),(k-1)*nu+j);
end
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -200,16 +203,11 @@ if order > 1
for j = 1:nu
flag = 0;
str1 = sprintf('%s,%s',subst_auxvar(k1(klag(k,1)),klag(k,2)-M_.maximum_lag-2), ...
M_.exo_names(j,:));
str = sprintf('%-20s',str1);
deblank(M_.exo_names(j,:)));
str = sprintf(label_format,str1);
for i=1:nvar
x = dr.ghxu(ivar(i),(k-1)*nu+j);
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
[str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_);
end
if flag
disp(str)
@ -253,3 +251,12 @@ for i = 1:length(M_.aux_vars)
end
error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
end
function [str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_)
if abs(x) > options_.dr_display_tol
flag = 1;
str = [str sprintf(value_format_float,x)];
else
str = [str sprintf(value_format_zero,0)];
end
end

9
matlab/distributions/rand_multivariate_student.m
View File

@ -1,4 +1,5 @@
function draw = rand_multivariate_student(Mean,Sigma_upper_chol,df)
% function draw = rand_multivariate_student(Mean,Sigma_upper_chol,df)
% Pseudo random draws from a multivariate student distribution,
% with expectation Mean, variance Sigma*df/(df-2) and degrees of freedom df>0.
%
@ -13,10 +14,14 @@ function draw = rand_multivariate_student(Mean,Sigma_upper_chol,df)
% draw [double] 1*n vector drawn from a multivariate normal distribution with expectation Mean and
% covariance Sigma.
%
% REMARK This is certainly not the most efficient way...
%
% NOTE See Zellner (appendix B.2, 1971) for a definition.
%
% Computes the t-distributed random numbers from
% X = \mu + Y\sqrt{\frac{\nu}{U}}
% where
% Y~N(0,Sigma) with Sigma=Sigma_upper_chol'*Sigma_upper_chol
% U~\Chi^2_{\nu}
% The latter is constructed as the sum of \nu standard normals.
% Copyright (C) 2003-2009 Dynare Team
%

2
matlab/dsge_likelihood.m
View File

@ -328,7 +328,7 @@ mm = length(T);
pp = DynareDataset.vobs;
rr = length(Q);
kalman_tol = DynareOptions.kalman_tol;
diffuse_kalman_tol = DynareOptions.kalman_tol;
diffuse_kalman_tol = DynareOptions.diffuse_kalman_tol;
riccati_tol = DynareOptions.riccati_tol;
Y = transpose(DynareDataset.data)-trend;

12
matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
View File

@ -1,10 +1,13 @@
function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,Steps,M_,options_,oo_,type)
% function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
% dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,Steps,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the conditional variance
% decomposition of the endogenous variables (or a subset of the endogenous variables).
%
% INPUTS
% SampleSize [integer] scalar, number of simulations.
% Steps [integers] horizons at which to conduct decomposition
% M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options.
% oo_ [structure] Dynare structure where the results are saved.
@ -16,7 +19,7 @@ function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
% vartan [char] array of characters (with nvar rows).
% NumberOfConditionalDecompFiles [integer] scalar, number of prior or posterior data files (for covariance).
% Copyright (C) 2009-2012 Dynare Team
% Copyright (C) 2009-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -47,6 +50,13 @@ else
error()
end
%delete old stale files before creating new ones
if posterior
delete_stale_file([M_.dname '/metropolis/' M_.fname '_PosteriorConditionalVarianceDecomposition*'])
else
delete_stale_file([M_.dname '/prior/moments/' M_.fname '_PriorConditionalVarianceDecomposition*'])
end
% Set varlist (vartan)
if ~posterior
if isfield(options_,'varlist')

33
matlab/dsge_simulated_theoretical_correlation.m
View File

@ -1,21 +1,23 @@
function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type)
% function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the endogenous
% variables second order moments.
% variables' second order moments.
%
% INPUTS
% SampleSize [integer]
% nar [integer]
% M_ [structure]
% options_ [structure]
% oo_ [structure]
% type [string]
% SampleSize [integer] scalar, number of simulations.
% nar [integer] maximum number of autocorrelations to
% consider
% M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options
% oo_ [structure] Dynare structure where the results are saved.
% type [string] 'prior' or 'posterior'
%
% OUTPUTS
% nvar [integer]
% vartan [char]
% CorrFileNumber [integer]
% Copyright (C) 2007-2012 Dynare Team
% nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows).
% CorrFileNumber [integer] scalar, number of prior or posterior data files (for correlation).
% Copyright (C) 2007-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -48,6 +50,13 @@ else
end
NumberOfDrawsFiles = length(DrawsFiles);
%delete old stale files before creating new ones
if posterior
delete_stale_file([M_.dname '/metropolis/' M_.fname '_PosteriorCorrelations*']);
else
delete_stale_file([M_.dname '/prior/moments/' M_.fname '_PriorCorrelations*']);
end
% Set varlist (vartan)
if ~posterior
if isfield(options_,'varlist')

10
matlab/dsge_simulated_theoretical_covariance.m
View File

@ -1,4 +1,5 @@
function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,type)
% function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the endogenous
% variables second order moments.
%
@ -15,7 +16,7 @@ function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(S
% vartan [char] array of characters (with nvar rows).
% CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance).
% Copyright (C) 2007-2012 Dynare Team
% Copyright (C) 2007-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -48,6 +49,13 @@ else
end
NumberOfDrawsFiles = length(DrawsFiles);
%delete old stale files before creating new ones
if posterior
delete_stale_file([M_.dname '/metropolis/' M_.fname '_Posterior2ndOrderMoments*'])
else
delete_stale_file([M_.dname '/prior/moments/' M_.fname '_Prior2ndOrderMoments*'])
end
% Set varlist (vartan)
if ~posterior
if isfield(options_,'varlist')

12
matlab/dsge_simulated_theoretical_variance_decomposition.m
View File

@ -1,5 +1,7 @@
function [nvar,vartan,NumberOfDecompFiles] = ...
dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,type)
% function [nvar,vartan,NumberOfDecompFiles] = ...
% dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the variance
% decomposition of the observed endogenous variables.
%
@ -16,7 +18,7 @@ function [nvar,vartan,NumberOfDecompFiles] = ...
% vartan [char] array of characters (with nvar rows).
% CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance).
% Copyright (C) 2007-2012 Dynare Team
% Copyright (C) 2007-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -47,7 +49,13 @@ else
disp('dsge_simulated_theoretical_variance_decomposition:: Unknown type!')
error()
end
NumberOfDrawsFiles = length(DrawsFiles);
%delete old stale files before creating new ones
if posterior
delete_stale_file([M_.dname '/metropolis/' M_.fname '_PosteriorVarianceDecomposition*']);
else
delete_stale_file([M_.dname '/prior/moments/' M_.fname '_PosteriorVarianceDecomposition*']);
end
% Set varlist (vartan)
if ~posterior

28
matlab/dsge_var_likelihood.m
View File

@ -239,10 +239,38 @@ else% Evaluation of the likelihood of the dsge-var model when the dsge prior wei
lik = .5*lik;% Minus likelihood
end
if isnan(lik)
info = 45;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if imag(lik)~=0
info = 46;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
% Add the (logged) prior density for the dsge-parameters.
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
fval = (lik-lnprior);
if isnan(fval)
info = 47;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if imag(fval)~=0
info = 48;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if (nargout == 8)
if isinf(dsge_prior_weight)
iXX = iGXX;

16
matlab/dyn_second_order_solver.m
View File

@ -1,7 +1,7 @@
function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_BC)
function dr = dyn_second_order_solver(jacobia,hessian_mat,dr,M_,threads_ABC,threads_BC)
%@info:
%! @deftypefn {Function File} {@var{dr} =} dyn_second_order_solver (@var{jacobia},@var{hessian},@var{dr},@var{M_},@var{threads_ABC},@var{threads_BC})
%! @deftypefn {Function File} {@var{dr} =} dyn_second_order_solver (@var{jacobia},@var{hessian_mat},@var{dr},@var{M_},@var{threads_ABC},@var{threads_BC})
%! @anchor{dyn_second_order_solver}
%! @sp 1
%! Computes the second order reduced form of the DSGE model
@ -11,7 +11,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
%! @table @ @var
%! @item jacobia
%! Matrix containing the Jacobian of the model
%! @item hessian
%! @item hessian_mat
%! Matrix containing the second order derivatives of the model
%! @item dr
%! Matlab's structure describing the reduced form solution of the model.
@ -73,7 +73,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
kk1 = reshape([1:nk^2],nk,nk);
kk1 = kk1(kk,kk);
% reordering second order derivatives
hessian = hessian(:,kk1(:));
hessian_mat = hessian_mat(:,kk1(:));
zx = zeros(np,np);
zu=zeros(np,M_.exo_nbr);
@ -91,7 +91,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
[nrzx,nczx] = size(zx);
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zx,threads_BC);
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
rhs = -rhs;
@ -118,7 +118,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
%ghxu
%rhs
hu = dr.ghu(nstatic+1:nstatic+nspred,:);
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zx,zu,threads_BC);
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
@ -136,7 +136,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
%ghuu
%rhs
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zu,threads_BC);
[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
[B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
@ -164,7 +164,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
hxx = dr.ghxx(nstatic+[1:nspred],:);
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
[B1, err] = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k2a,:),threads_BC);
[B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;

13
matlab/dynare.m
View File

@ -96,6 +96,10 @@ end
% Testing if file have extension
% If no extension default .mod is added
dot_location=(strfind(fname,'.'));
if length(dot_location)>1
error('DYNARE: Periods in filenames are only allowed for .mod or .dyn extensions')
end
if isempty(strfind(fname,'.'))
fnamelength = length(fname);
fname1 = [fname '.dyn'];
@ -106,9 +110,10 @@ if isempty(strfind(fname,'.'))
fname = fname1;
% Checking file extension
else
if ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
if dot_location~=length(fname)-3 ... %if the file name has fewer than 4 characters and there is a period
|| ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
&& ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.DYN')
error('DYNARE: argument must be a filename with .mod or .dyn extension')
error('DYNARE: argument must be a filename with .mod or .dyn extension and must not include any other periods')
end;
fnamelength = length(fname) - 4;
end;
@ -155,6 +160,10 @@ if ~exist(fname,'file') || isequal(fname,'dir')
error(['dynare:: can''t open ' fname])
end
if ~isvarname(fname(1:end-4))
error('DYNARE: argument of dynare must conform to Matlab''s convention for naming functions, i.e. start with a letter and not contain special characters. Please rename your MOD-file.')
end
% pre-dynare-preprocessor-hook
if exist(fname(1:end-4),'dir') && exist([fname(1:end-4) filesep 'hooks'],'dir') && exist([fname(1:end-4) filesep 'hooks/priorprocessing.m'],'file')
run([fname(1:end-4) filesep 'hooks/priorprocessing'])

26
matlab/dynare_estimation.m
View File

@ -1,9 +1,10 @@
function oo_recursive_=dynare_estimation(var_list,dname)
% function dynare_estimation(var_list)
% function dynare_estimation(var_list, dname)
% runs the estimation of the model
%
% INPUTS
% var_list: selected endogenous variables vector
% dname: alternative directory name
%
% OUTPUTS
% oo_recursive_: cell array containing the results structures from recursive estimation
@ -11,7 +12,7 @@ function oo_recursive_=dynare_estimation(var_list,dname)
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2014 Dynare Team
% Copyright (C) 2003-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -28,7 +29,7 @@ function oo_recursive_=dynare_estimation(var_list,dname)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global options_ oo_ M_
global options_ oo_ M_ dataset_
oo_recursive_={};
@ -55,7 +56,7 @@ nnobs = length(nobs);
horizon = options_.forecast;
if nargin<2 || ~exist(dname) || isempty(dname)
dname = M_.fname;
dname = options_.dirname;
end
M_.dname = dname;
@ -91,20 +92,12 @@ else
dynare_estimation_1(var_list,dname);
end
if options_.mode_compute && options_.analytic_derivation,
if isnumeric(options_.mode_compute) && options_.mode_compute && options_.analytic_derivation,
options_.analytic_derivation=analytic_derivation0;
end
if nnobs > 1 && horizon > 0
mh_replic = options_.mh_replic;
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
gend = options_.nobs;
data_plot_end_point=min(options_.first_obs+gend-1+horizon,size(rawdata,1)); %compute last observation that can be plotted
rawdata = rawdata(options_.first_obs:data_plot_end_point,:);
% Take the log of the variables if needed
if options_.loglinear && ~options_.logdata % and if the data are not in logs, then...
rawdata = log(rawdata);
end
endo_names = M_.endo_names;
n_varobs = length(options_.varobs);
@ -132,11 +125,12 @@ if nnobs > 1 && horizon > 0
IdObs(j,1) = iobs;
end
end
gend = dataset_.nobs;
time_offset=min(3,gend-1); %for observables, plot 3 previous periods unless data is shorter
k = time_offset+min(nobs(end)-nobs(1)+horizon, ...
size(rawdata,1)-nobs(1));
data2 = rawdata(end-k+1:end,:);
size(dataset_.data,1)-nobs(1));
data2 = dataset_.data(end-k+1:end,:);
[nbplt,nr,nc,lr,lc,nstar] = pltorg(nvar);
m = 1;
plot_index=0;

10
matlab/dynare_estimation_1.m
View File

@ -261,14 +261,14 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[xparam1, fval, exitflag, hh, options_, Scale] = dynare_minimize_objective(objective_function,xparam1,options_.mode_compute,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
if options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
if isnumeric(options_.mode_compute) && options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
options_.analytic_derivation = options_analytic_derivation_old; %reset
elseif options_.mode_compute==6 %save scaling factor
elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
options_.mh_jscale = Scale;
bayestopt_.jscale = ones(length(xparam1),1)*Scale;
end
if ~isequal(options_.mode_compute,6) %always already computes covariance matrix
if ~isnumeric(options_.mode_compute) || ~isequal(options_.mode_compute,6) %always already computes covariance matrix
if options_.cova_compute == 1 %user did not request covariance not to be computed
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
ana_deriv_old = options_.analytic_derivation;
@ -276,12 +276,12 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
elseif ~(isequal(options_.mode_compute,5) && newratflag~=1),
elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1),
% with flag==0, we force to use the hessian from outer
% product gradient of optimizer 5
hh = reshape(hessian(objective_function,xparam1, ...
options_.gstep,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_),nx,nx);
elseif isequal(options_.mode_compute,5)
elseif isnumeric(options_.mode_compute) && isequal(options_.mode_compute,5)
% other numerical hessian options available with optimizer 5
%
% if newratflag == 0

5
matlab/dynare_sensitivity.m
View File

@ -163,6 +163,11 @@ options_gsa = set_default_option(options_gsa,'istart_rmse',options_.presample+1)
options_gsa = set_default_option(options_gsa,'alpha_rmse',0.001);
options_gsa = set_default_option(options_gsa,'alpha2_rmse',1.e-5);
if options_gsa.neighborhood_width,
options_gsa.pprior=0;
options_gsa.ppost=0;
end
if options_gsa.redform && options_gsa.neighborhood_width==0 && isempty(options_gsa.threshold_redform),
options_gsa.pprior=1;
options_gsa.ppost=0;

4
matlab/dynasave.m
View File

@ -35,6 +35,10 @@ if size(var_list,1) == 0
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
end
if ~isfield(oo_,'endo_simul') || isempty(oo_.endo_simul)
error('dynasave:: The results structure does not contain simulated series. Maybe the periods option has not been set.')
end
n = size(var_list,1);
ivar=zeros(n,1);
for i=1:n

67
matlab/dyntable.m
View File

@ -1,7 +1,16 @@
function dyntable(title,headers,labels,values,label_width,val_width, ...
val_precis)
% Copyright (C) 2002-2013 Dynare Team
function dyntable(title,headers,labels,values,label_width,val_width,val_precis)
% function dyntable(title,headers,labels,values,label_width,val_width,val_precis)
% Inputs:
% title [string] Table title
% headers [n by nchar] character array of labels for header row
% labels [n by nchar] character array of labels for label column
% values [matrix] matrix of values to display
% label_width [scalar] Width of the label
% val_width [scalar] Width of value column
% val_precis [integer] precision of displayed values
%
%
% Copyright (C) 2002-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -24,48 +33,46 @@ if options_.noprint
return
end
%label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
% label_width);
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
label_fmt = sprintf('%%-%ds',label_width);
%% get width of label column
if ~isempty(label_width)
label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
label_width);
else %use default length
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
end
label_format_leftbound = sprintf('%%-%ds',label_width);
%% get width of label column
if all(~isfinite(values))
values_length = 4;
else
values_length = max(ceil(max(max(log10(abs(values(isfinite(values))))))),1)+val_precis+1;
end
if any(values) < 0
if any(values) < 0 %add one character for minus sign
values_length = values_length+1;
end
headers_length = max(size(deblank(headers(2:end,:)),2));
%val_width = max(values_length,val_width);
val_width = max(headers_length,values_length)+2;
%header_fmt = sprintf('%%-%ds',val_width);
val_fmt = sprintf('%%%d.%df',val_width,val_precis);
headers_offset = 0;
values_offset = 0;
if headers_length > values_length
% values_offset = ceil((val_width-values_length)/2);
%% get width of header strings
headers_length = max(size(deblank(headers(2:end,:)),2));
if ~isempty(val_width)
val_width = max(max(headers_length,values_length)+2,val_width);
else
val_width = max(headers_length,values_length)+2;
end
value_format = sprintf('%%%d.%df',val_width,val_precis);
header_string_format = sprintf('%%%ds',val_width);
if length(title) > 0
disp(sprintf('\n\n%s\n',title));
fprintf('\n\n%s\n',title);
end
%Create and print header string
if length(headers) > 0
hh = sprintf(label_fmt,headers(1,:));
hh = sprintf(label_format_leftbound ,deblank(headers(1,:)));
for i=2:size(headers,1)
hla = size(deblank(headers(i,:)),2);
hlb = ceil((val_width - hla)/2);
hla = val_width - hla - hlb;
hh = [hh char(32*ones(1,hlb)) deblank(headers(i,:)) ...
char(32*ones(1,hla))];
hh = [hh sprintf(header_string_format,deblank(headers(i,:)))];
end
disp(hh);
end
for i=1:size(values,1)
disp([sprintf(label_fmt,deblank(labels(i,:))) char(32*ones(1,values_offset)) sprintf(val_fmt,values(i,:))]);
end
% 10/30/02 MJ
disp([sprintf(label_format_leftbound ,deblank(labels(i,:))) sprintf(value_format ,values(i,:))]);
end

29
matlab/global_initialization.m
View File

@ -33,6 +33,7 @@ global oo_ M_ options_ estim_params_ bayestopt_ estimation_info ex0_ ys0_
estim_params_ = [];
bayestopt_ = [];
options_.datafile = '';
options_.dirname = M_.fname;
options_.dataset = [];
options_.verbosity = 1;
options_.terminal_condition = 0;
@ -55,6 +56,7 @@ options_.qz_zero_threshold = 1e-6;
options_.lyapunov_complex_threshold = 1e-15;
options_.solve_tolf = eps^(1/3);
options_.solve_tolx = eps^(2/3);
options_.dr_display_tol=1e-6;
options_.dp.maxit = 3000;
options_.steady.maxit = 50;
@ -66,6 +68,8 @@ options_.mode_check.symmetric_plots = 1;
options_.mode_check.number_of_points = 20;
options_.mode_check.nolik = 0;
options_.huge_number = 1e7;
% Default number of threads for parallelized mex files.
options_.threads.kronecker.A_times_B_kronecker_C = 1;
options_.threads.kronecker.sparse_hessian_times_B_kronecker_C = 1;
@ -409,6 +413,7 @@ options_.recursive_estimation_restart = 0;
options_.MCMC_jumping_covariance='hessian';
options_.use_calibration_initialization = 0;
options_.endo_vars_for_moment_computations_in_estimation=[];
options_.TaRB.use_TaRB = 0;
% Prior restrictions
options_.prior_restrictions.status = 0;
@ -525,6 +530,30 @@ simpsa.MAX_FUN_EVALS = 20000;
simpsa.DISPLAY = 'iter';
options_.simpsa = simpsa;
%solveopt optimizer
solveopt.minimizer_indicator=-1; %use minimizer
solveopt.TolX=1e-6; %accuracy of argument
solveopt.TolFun=1e-6; %accuracy of function
solveopt.MaxIter=15000;
solveopt.verbosity=1;
solveopt.TolXConstraint=1.e-8;
solveopt.SpaceDilation=2.5;
solveopt.LBGradientStep=1.e-11;
options_.solveopt=solveopt;
%simulated annealing
options_.saopt.neps=10;
options_.saopt.maximizer_indicator=0;
options_.saopt.rt=0.10;
options_.saopt.MaxIter=100000;
options_.saopt.verbosity=1;
options_.saopt.TolFun=1.0e-8;
options_.saopt.initial_temperature=15;
options_.saopt.ns=10;
options_.saopt.nt=10;
options_.saopt.step_length_c=0.1;
options_.saopt.initial_step_length=1;
% prior analysis
options_.prior_mc = 20000;
options_.prior_analysis_endo_var_list = [];

337
matlab/gsa/filt_mc_.m
View File

@ -43,7 +43,7 @@ alpha = options_gsa_.alpha_rmse;
% alpha2 = options_gsa_.alpha2_rmse;
alpha2 = 0;
pvalue = options_gsa_.alpha2_rmse;
istart = options_gsa_.istart_rmse;
istart = max(2,options_gsa_.istart_rmse);
alphaPC = 0.5;
fname_ = M_.fname;
@ -242,41 +242,66 @@ end
if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
if options_.opt_gsa.pprior
anam='rmse_prior_post';
atitle='RMSE prior: Log Posterior Kernel';
else
anam='rmse_mc_post';
atitle='RMSE MC: Log Posterior Kernel';
end
stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), anam, 1,[],OutDir);
stab_map_2(x(ipost(1:nfilt),:),alpha2,pvalue,anam, OutDir);
options_mcf.pvalue_ks = alpha;
options_mcf.pvalue_corr = pvalue;
options_mcf.alpha2 = alpha2;
options_mcf.param_names = char(bayestopt_.name);
options_mcf.fname_ = fname_;
options_mcf.OutputDirectoryName = OutDir;
options_mcf.amcf_name = anam;
options_mcf.amcf_title = atitle;
options_mcf.title = atitle;
options_mcf.beha_title = 'better posterior kernel';
options_mcf.nobeha_title = 'worse posterior kernel';
mcf_analysis(x, ipost(1:nfilt), ipost(nfilt+1:end), options_mcf, options_);
if options_.opt_gsa.pprior
anam='rmse_prior_lik';
anam = 'rmse_prior_lik';
atitle = 'RMSE prior: Log Likelihood Kernel';
else
anam='rmse_mc_lik';
atitle = 'RMSE MC: Log Likelihood Kernel';
end
stab_map_1(x, ilik(1:nfilt), ilik(nfilt+1:end), anam, 1,[],OutDir);
stab_map_2(x(ilik(1:nfilt),:),alpha2,pvalue,anam, OutDir);
options_mcf.amcf_name = anam;
options_mcf.amcf_title = atitle;
options_mcf.title = atitle;
options_mcf.beha_title = 'better likelihood';
options_mcf.nobeha_title = 'worse likelihood';
mcf_analysis(x, ilik(1:nfilt), ilik(nfilt+1:end), options_mcf, options_);
else
for i=1:size(vvarvecm,1),
[dum, ixx(:,i)]=sort(rmse_MC(:,i));
if options_.opt_gsa.ppost,
if options_.opt_gsa.ppost,
rmse_txt=rmse_pmean;
r2_txt=r2_pmean;
else
if options_.opt_gsa.pprior || ~exist('rmse_pmean'),
if exist('rmse_mode'),
rmse_txt=rmse_mode;
r2_txt=r2_mode;
else
rmse_txt=NaN(1,size(rmse_MC,2));
r2_txt=NaN(1,size(r2_MC,2));
end
else
%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
rmse_txt=rmse_pmean;
r2_txt=r2_pmean;
else
if options_.opt_gsa.pprior || ~exist('rmse_pmean'),
if exist('rmse_mode'),
rmse_txt=rmse_mode;
r2_txt=r2_mode;
else
rmse_txt=NaN(1,size(rmse_MC,2));
r2_txt=NaN(1,size(r2_MC,2));
end
else
%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
rmse_txt=rmse_pmean;
r2_txt=r2_pmean;
end
end
for j=1:npar+nshock,
end
for i=1:size(vvarvecm,1),
[dum, ixx(:,i)]=sort(rmse_MC(:,i));
end
PP=ones(npar+nshock,size(vvarvecm,1));
PPV=ones(size(vvarvecm,1),size(vvarvecm,1),npar+nshock);
SS=zeros(npar+nshock,size(vvarvecm,1));
for j=1:npar+nshock,
for i=1:size(vvarvecm,1),
[H,P,KSSTAT] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
[H1,P1,KSSTAT1] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
[H2,P2,KSSTAT2] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
@ -289,6 +314,22 @@ else
end
PP(j,i)=P;
end
for i=1:size(vvarvecm,1),
for l=1:size(vvarvecm,1),
if l~=i && PP(j,i)<alpha && PP(j,l)<alpha
[H,P,KSSTAT] = smirnov(x(ixx(1:nfilt0(i),i),j),x(ixx(1:nfilt0(l),l),j), alpha);
%[H1,P1,KSSTAT1] = smirnov(x(ixx(1:nfilt0(i),i),j),x(:,j), alpha);
% PP(j,i)=min(P,PP(j,i));
% PP(j,l)=min(P,PP(j,l));
%if P<P1
% if SS(j,i)*SS(j,l)
PPV(i,l,j) = P;
% end
elseif l==i
PPV(i,l,j) = PP(j,i);
end
end
end
end
if ~options_.nograph,
ifig=0;
@ -308,12 +349,17 @@ else
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(lnprior(ixx(1:nfilt0(i),i)));
set(h,'color','red')
hold on, cumplot(lnprior)
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(lnprior);
set(h,'color','k','linewidth',1)
h=cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
set(h,'color','green')
set(h,'color','red','linewidth',2)
title(vvarvecm(i,:),'interpreter','none')
if mod(i,9)==0 || i==size(vvarvecm,1)
if ~isoctave
annotation('textbox', [0.1,0,0.35,0.05],'String', 'Log-prior for BETTER R2','Color','Blue','horizontalalignment','center');
annotation('textbox', [0.55,0,0.35,0.05],'String', 'Log-prior for WORSE R2', 'Color','Red','horizontalalignment','center');
end
if options_.opt_gsa.ppost
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnprior',int2str(ifig)],options_);
else
@ -342,15 +388,20 @@ else
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(likelihood(ixx(1:nfilt0(i),i)));
set(h,'color','red')
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(likelihood);
set(h,'color','k','linewidth',1)
h=cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
set(h,'color','green')
set(h,'color','red','linewidth',2)
title(vvarvecm(i,:),'interpreter','none')
if options_.opt_gsa.ppost==0,
set(gca,'xlim',[min( likelihood(ixx(1:nfilt0(i),i)) ) max( likelihood(ixx(1:nfilt0(i),i)) )])
end
if mod(i,9)==0 || i==size(vvarvecm,1)
if ~isoctave
annotation('textbox', [0.1,0,0.35,0.05],'String', 'Log-likelihood for BETTER R2','Color','Blue','horizontalalignment','center');
annotation('textbox', [0.55,0,0.35,0.05],'String', 'Log-likelihood for WORSE R2', 'Color','Red','horizontalalignment','center');
end
if options_.opt_gsa.ppost
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnlik',int2str(ifig) ],options_);
else
@ -379,15 +430,20 @@ else
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(logpo2(ixx(1:nfilt0(i),i)));
set(h,'color','red')
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(logpo2);
set(h,'color','k','linewidth',1)
h=cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
set(h,'color','green')
set(h,'color','red','linewidth',2)
title(vvarvecm(i,:),'interpreter','none')
if options_.opt_gsa.ppost==0,
set(gca,'xlim',[min( logpo2(ixx(1:nfilt0(i),i)) ) max( logpo2(ixx(1:nfilt0(i),i)) )])
end
if mod(i,9)==0 || i==size(vvarvecm,1)
if ~isoctave
annotation('textbox', [0.1,0,0.35,0.05],'String', 'Log-posterior for BETTER R2','Color','Blue','horizontalalignment','center');
annotation('textbox', [0.55,0,0.35,0.05],'String', 'Log-posterior for WORSE R2', 'Color','Red','horizontalalignment','center');
end
if options_.opt_gsa.ppost
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnpost',int2str(ifig) ],options_);
else
@ -401,11 +457,12 @@ else
end
end
param_names='';
for j=1:npar+nshock,
param_names=char(param_names, bayestopt_.name{j});
end
param_names=param_names(2:end,:);
param_names=char(bayestopt_.name);
% param_names='';
% for j=1:npar+nshock,
% param_names=char(param_names, bayestopt_.name{j});
% end
% param_names=param_names(2:end,:);
skipline()
disp('RMSE over the MC sample:')
@ -526,15 +583,94 @@ else
a00=jet(size(vvarvecm,1));
if options_.opt_gsa.ppost
temp_name='RMSE Posterior Tradeoffs:';
atitle='RMSE Posterior Map:';
asname='rmse_post';
else
if options_.opt_gsa.pprior
temp_name='RMSE Prior Tradeoffs:';
atitle='RMSE Prior Map:';
asname='rmse_prior';
else
temp_name='RMSE MC Tradeoffs;';
temp_name='RMSE MC Tradeoffs:';
atitle='RMSE MC Map:';
asname='rmse_mc';
end
end
% now I plot by observed variables
options_mcf.pvalue_ks = alpha;
options_mcf.pvalue_corr = pvalue;
options_mcf.alpha2 = alpha2;
options_mcf.param_names = char(bayestopt_.name);
options_mcf.fname_ = fname_;
options_mcf.OutputDirectoryName = OutDir;
for iy=1:size(vvarvecm,1),
options_mcf.amcf_name = [asname '_' deblank(vvarvecm(iy,:)) '_map' ];
options_mcf.amcf_title = [atitle ' ' deblank(vvarvecm(iy,:))];
options_mcf.beha_title = ['better fit of ' deblank(vvarvecm(iy,:))];
options_mcf.nobeha_title = ['worse fit of ' deblank(vvarvecm(iy,:))];
options_mcf.title = ['the fit of ' deblank(vvarvecm(iy,:))];
mcf_analysis(x, ixx(1:nfilt0(iy),iy), ixx(nfilt0(iy)+1:end,iy), options_mcf, options_);
end
for iy=1:size(vvarvecm,1),
ipar = find(any(squeeze(PPV(iy,:,:))<alpha));
for ix=1:ceil(length(ipar)/5),
hh = dyn_figure(options_,'name',[temp_name,' observed variable ',deblank(vvarvecm(iy,:))]);
for j=1+5*(ix-1):min(length(ipar),5*ix),
subplot(2,3,j-5*(ix-1))
%h0=cumplot(x(:,nsnam(j)+nshock));
h0=cumplot(x(:,ipar(j)));
set(h0,'color',[0 0 0])
hold on,
iobs=find(squeeze(PPV(iy,:,ipar(j)))<alpha);
for i=1:size(vvarvecm,1),
%h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock));
% h0=cumplot(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)));
if any(iobs==i) || i==iy,
h0=cumplot(x(ixx(1:nfilt0(i),i),ipar(j)));
if ~isoctave
hcmenu = uicontextmenu;
uimenu(hcmenu,'Label',deblank(vvarvecm(i,:)));
set(h0,'uicontextmenu',hcmenu)
end
else
h0=cumplot(x(ixx(1:nfilt0(i),i),ipar(j))*NaN);
end
set(h0,'color',a00(i,:),'linewidth',2)
end
ydum=get(gca,'ylim');
%xdum=xparam1(nshock+nsnam(j));
if exist('xparam1')
xdum=xparam1(ipar(j));
h1=plot([xdum xdum],ydum);
set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
end
xlabel('')
title([pnam{ipar(j)}],'interpreter','none')
end
%subplot(3,2,6)
if isoctave
legend(char('base',vvarvecm),'location','eastoutside');
else
h0=legend(char('base',vvarvecm),0);
set(h0,'fontsize',6,'position',[0.7 0.1 0.2 0.3],'interpreter','none');
end
%h0=legend({'base',vnam{np}}',0);
%set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
if options_.opt_gsa.ppost
dyn_saveas(hh,[ OutDir filesep fname_ '_rmse_post_' deblank(vvarvecm(iy,:)) '_' int2str(ix)],options_);
else
if options_.opt_gsa.pprior
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_' deblank(vvarvecm(iy,:)) '_' int2str(ix) ],options_);
else
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_' deblank(vvarvecm(iy,:)) '_' int2str(ix)],options_);
end
end
end
end
% now I plot by individual parameters
for ix=1:ceil(length(nsnam)/5),
hh = dyn_figure(options_,'name',[temp_name,' observed variables ',int2str(ix)]);
hh = dyn_figure(options_,'name',[temp_name,' estimated params and shocks ',int2str(ix)]);
for j=1+5*(ix-1):min(size(snam2,1),5*ix),
subplot(2,3,j-5*(ix-1))
%h0=cumplot(x(:,nsnam(j)+nshock));
@ -551,8 +687,13 @@ else
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))*NaN);
else
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j)));
end
set(h0,'color',a00(i,:))
if ~isoctave
hcmenu = uicontextmenu;
uimenu(hcmenu,'Label',deblank(vvarvecm(i,:)));
set(h0,'uicontextmenu',hcmenu)
end
end
set(h0,'color',a00(i,:),'linewidth',2)
end
ydum=get(gca,'ylim');
%xdum=xparam1(nshock+nsnam(j));
@ -574,74 +715,74 @@ else
%h0=legend({'base',vnam{np}}',0);
%set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
if options_.opt_gsa.ppost
dyn_saveas(hh,[ OutDir filesep fname_ '_rmse_post_' int2str(ix)],options_);
dyn_saveas(hh,[ OutDir filesep fname_ '_rmse_post_params_' int2str(ix)],options_);
else
if options_.opt_gsa.pprior
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_' int2str(ix) ],options_);
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_params_' int2str(ix) ],options_);
else
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_' int2str(ix)],options_);
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_params_' int2str(ix)],options_);
end
end
end
end
for j=1:size(SP,2),
nsx(j)=length(find(SP(:,j)));
end
% for j=1:size(SP,2),
% nsx(j)=length(find(SP(:,j)));
% end
skipline(2)
disp('Sensitivity table (significance and direction):')
vav=char(zeros(1, size(param_names,2)+3 ));
ibl = 12-size(vvarvecm,2);
for j=1:size(vvarvecm,1),
vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
end
disp(vav)
for j=1:npar+nshock, %estim_params_.np,
%disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
end
% skipline(2)
% disp('Sensitivity table (significance and direction):')
% vav=char(zeros(1, size(param_names,2)+3 ));
% ibl = 12-size(vvarvecm,2);
% for j=1:size(vvarvecm,1),
% vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
% end
% disp(vav)
% for j=1:npar+nshock, %estim_params_.np,
% %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
% disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
% disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
% end
skipline()
disp('Starting bivariate analysis:')
for i=1:size(vvarvecm,1)
if options_.opt_gsa.ppost
fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
else
if options_.opt_gsa.pprior
fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
else
fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
end
end
stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
% [pc,latent,explained] = pcacov(c0);
% %figure, bar([explained cumsum(explained)])
% ifig=0;
% j2=0;
% for j=1:npar+nshock,
% i2=find(abs(pc(:,j))>alphaPC);
% if ~isempty(i2),
% j2=j2+1;
% if mod(j2,12)==1,
% ifig=ifig+1;
% figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
% end
% subplot(3,4,j2-(ifig-1)*12)
% bar(pc(i2,j)),
% set(gca,'xticklabel',bayestopt_.name(i2)),
% set(gca,'xtick',[1:length(i2)])
% title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
% end
% if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
% saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
% end
% end
% close all
end
% skipline()
% disp('Starting bivariate analysis:')
%
% for i=1:size(vvarvecm,1)
% if options_.opt_gsa.ppost
% fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
% else
% if options_.opt_gsa.pprior
% fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
% else
% fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
% end
% end
% stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
%
% % [pc,latent,explained] = pcacov(c0);
% % %figure, bar([explained cumsum(explained)])
% % ifig=0;
% % j2=0;
% % for j=1:npar+nshock,
% % i2=find(abs(pc(:,j))>alphaPC);
% % if ~isempty(i2),
% % j2=j2+1;
% % if mod(j2,12)==1,
% % ifig=ifig+1;
% % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
% % end
% % subplot(3,4,j2-(ifig-1)*12)
% % bar(pc(i2,j)),
% % set(gca,'xticklabel',bayestopt_.name(i2)),
% % set(gca,'xtick',[1:length(i2)])
% % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
% % end
% % if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
% % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
% % end
% % end
% % close all
% end
end

9
matlab/gsa/log_trans_.m
View File

@ -1,4 +1,4 @@
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0,isig,lam)
% Copyright (C) 2012 Dynare Team
%
@ -17,6 +17,12 @@ function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==4,
% inverse transformation
yy = (exp(y0)-lam)*isig;
return
end
if nargin==1,
xdir0='';
end
@ -67,5 +73,6 @@ else
lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
end
end
lam = max(lam,0);
yy = log(y0+lam);
end

423
matlab/gsa/map_calibration.m
View File

@ -55,7 +55,7 @@ else
load(filetoload,'lpmat','lpmat0','istable','iunstable','iindeterm','iwrong' ,'infox')
lpmat = [lpmat0 lpmat];
type = 'mc';
end
end
end
[Nsam, np] = size(lpmat);
npar = size(pnames,1);
@ -65,58 +65,91 @@ nbr_irf_restrictions = size(DynareOptions.endogenous_prior_restrictions.irf,1);
nbr_moment_restrictions = size(DynareOptions.endogenous_prior_restrictions.moment,1);
if init
mat_irf=cell(nbr_irf_restrictions,1);
for ij=1:nbr_irf_restrictions,
mat_irf{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.irf{ij,3}));
end
mat_moment=cell(nbr_moment_restrictions,1);
for ij=1:nbr_moment_restrictions,
mat_moment{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.moment{ij,3}));
end
irestrictions = [1:Nsam];
h = dyn_waitbar(0,'Please wait...');
for j=1:Nsam,
Model = set_all_parameters(lpmat(j,:)',EstimatedParameters,Model);
[Tt,Rr,SteadyState,info] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
if info(1)==0,
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults);
if ~isempty(info_irf)
for ij=1:nbr_irf_restrictions,
mat_irf{ij}(j,:)=data_irf{ij}(:,2)';
end
indx_irf(j,:)=info_irf(:,1);
end
if ~isempty(info_moment)
for ij=1:nbr_moment_restrictions,
mat_moment{ij}(j,:)=data_moment{ij}(:,2)';
end
indx_moment(j,:)=info_moment(:,1);
end
else
irestrictions(j)=0;
mat_irf=cell(nbr_irf_restrictions,1);
for ij=1:nbr_irf_restrictions,
mat_irf{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.irf{ij,3}));
end
dyn_waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
end
dyn_waitbar_close(h);
irestrictions=irestrictions(find(irestrictions));
xmat=lpmat(irestrictions,:);
skipline()
endo_prior_restrictions=DynareOptions.endogenous_prior_restrictions;
save([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
else
load([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
end
if ~isempty(indx_irf),
% For single legend search which has maximum nbr of restrictions
mat_moment=cell(nbr_moment_restrictions,1);
for ij=1:nbr_moment_restrictions,
mat_moment{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.moment{ij,3}));
end
irestrictions = [1:Nsam];
h = dyn_waitbar(0,'Please wait...');
for j=1:Nsam,
Model = set_all_parameters(lpmat(j,:)',EstimatedParameters,Model);
if nbr_moment_restrictions,
[Tt,Rr,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
else
[Tt,Rr,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
end
if info(1)==0,
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults);
if ~isempty(info_irf)
for ij=1:nbr_irf_restrictions,
mat_irf{ij}(j,:)=data_irf{ij}(:,2)';
end
indx_irf(j,:)=info_irf(:,1);
end
if ~isempty(info_moment)
for ij=1:nbr_moment_restrictions,
mat_moment{ij}(j,:)=data_moment{ij}(:,2)';
end
indx_moment(j,:)=info_moment(:,1);
end
else
irestrictions(j)=0;
end
dyn_waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
end
dyn_waitbar_close(h);
irestrictions=irestrictions(find(irestrictions));
xmat=lpmat(irestrictions,:);
skipline()
endo_prior_restrictions=DynareOptions.endogenous_prior_restrictions;
save([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
else
load([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
end
if ~isempty(indx_irf),
skipline()
disp('Deleting old IRF calibration plots ...')
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_calib*.eps']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_calib*.fig']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_calib*.pdf']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions.eps']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions.fig']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions.pdf']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
disp('done !')
skipline()
% For single legend search which has maximum nbr of restrictions
all_irf_couples = cellstr([char(endo_prior_restrictions.irf(:,1)) char(endo_prior_restrictions.irf(:,2))]);
irf_couples = unique(all_irf_couples);
nbr_irf_couples = size(irf_couples,1);
plot_indx = NaN(nbr_irf_couples,1);
time_matrix=cell(nbr_irf_couples,1);
indx_irf_matrix=zeros(length(irestrictions),nbr_irf_couples);
irf_matrix=cell(nbr_irf_couples,1);
irf_mean=cell(nbr_irf_couples,1);
irf_median=cell(nbr_irf_couples,1);
@ -131,17 +164,21 @@ if ~isempty(indx_irf),
plot_indx(ij) = find(strcmp(irf_couples,all_irf_couples(ij,:)));
time_matrix{plot_indx(ij)} = [time_matrix{plot_indx(ij)} endo_prior_restrictions.irf{ij,3}];
end
iplot_indx = ones(size(plot_indx));
indx_irf = indx_irf(irestrictions,:);
h1=dyn_figure(DynareOptions,'name',[type ' evaluation of irf restrictions']);
nrow=ceil(sqrt(nbr_irf_couples));
ncol=nrow;
if nrow*(nrow-1)>nbr_irf_couples,
ncol=nrow-1;
if ~DynareOptions.nograph,
h1=dyn_figure(DynareOptions,'name',[type ' evaluation of irf restrictions']);
nrow=ceil(sqrt(nbr_irf_couples));
ncol=nrow;
if nrow*(nrow-1)>nbr_irf_couples,
ncol=nrow-1;
end
end
for ij=1:nbr_irf_restrictions,
mat_irf{ij}=mat_irf{ij}(irestrictions,:);
irf_matrix{plot_indx(ij)} = [irf_matrix{plot_indx(ij)} mat_irf{ij}];
indx_irf_matrix(:,plot_indx(ij)) = indx_irf_matrix(:,plot_indx(ij)) + indx_irf(:,ij);
for ik=1:size(mat_irf{ij},2),
[Mean,Median,Var,HPD,Distrib] = ...
posterior_moments(mat_irf{ij}(:,ik),0,DynareOptions.mh_conf_sig);
@ -156,18 +193,21 @@ if ~isempty(indx_irf),
if size(mat_irf{ij},2)>1,
leg = [leg,':' ,num2str(endo_prior_restrictions.irf{ij,3}(end))];
aleg = [aleg,'-' ,num2str(endo_prior_restrictions.irf{ij,3}(end))];
end
if length(time_matrix{plot_indx(ij)})==1,
figure(h1),
iplot_indx(ij)=0;
end
if ~DynareOptions.nograph && length(time_matrix{plot_indx(ij)})==1,
set(0,'currentfigure',h1),
subplot(nrow,ncol, plot_indx(ij)),
hc = cumplot(mat_irf{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold all,
a=axis;
delete(hc);
x1val=max(endo_prior_restrictions.irf{ij,4}(1),a(1));
x2val=min(endo_prior_restrictions.irf{ij,4}(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceAlpha', 0.5)
hold all,
set(hp,'FaceColor', [0.7 0.8 1])
hc = cumplot(mat_irf{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold off,
% hold off,
title([endo_prior_restrictions.irf{ij,1},' vs ',endo_prior_restrictions.irf{ij,2}, '(', leg,')'],'interpreter','none'),
@ -185,75 +225,133 @@ if ~isempty(indx_irf),
indx1 = find(indx_irf(:,ij)==0);
indx2 = find(indx_irf(:,ij)~=0);
atitle0=[endo_prior_restrictions.irf{ij,1},' vs ',endo_prior_restrictions.irf{ij,2}, '(', leg,')'];
fprintf(['%4.1f%% of the prior support matches IRF ',atitle0,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,endo_prior_restrictions.irf{ij,4})
fprintf(['%4.1f%% of the ',type,' support matches IRF ',atitle0,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,endo_prior_restrictions.irf{ij,4})
% aname=[type '_irf_calib_',int2str(ij)];
aname=[type '_irf_calib_',endo_prior_restrictions.irf{ij,1},'_vs_',endo_prior_restrictions.irf{ij,2},'_',aleg];
atitle=[type ' IRF Calib: Parameter(s) driving ',endo_prior_restrictions.irf{ij,1},' vs ',endo_prior_restrictions.irf{ij,2}, '(', leg,')'];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'IRF prior restriction';
options_mcf.nobeha_title = 'NO IRF prior restriction';
options_mcf.beha_title = 'IRF restriction';
options_mcf.nobeha_title = 'NO IRF restriction';
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, DynareOptions);
end
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
% stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
% end
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
% stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
% end
end
figure(h1);
for ij=1:nbr_irf_couples,
if length(time_matrix{ij})>1,
subplot(nrow,ncol, ij)
itmp = (find(plot_indx==ij));
plot(time_matrix{ij},[max(irf_matrix{ij})' min(irf_matrix{ij})'],'k--','linewidth',2)
hold on,
plot(time_matrix{ij},irf_median{ij},'k','linewidth',2)
plot(time_matrix{ij},[irf_distrib{ij}],'k-')
a=axis;
tmp=[];
for ir=1:length(itmp),
for it=1:length(endo_prior_restrictions.irf{itmp(ir),3})
temp_index = find(time_matrix{ij}==endo_prior_restrictions.irf{itmp(ir),3}(it));
tmp(temp_index,:) = endo_prior_restrictions.irf{itmp(ir),4};
if ~DynareOptions.nograph,
set(0,'currentfigure',h1);
subplot(nrow,ncol, ij)
itmp = (find(plot_indx==ij));
htmp = plot(time_matrix{ij},[max(irf_matrix{ij})' min(irf_matrix{ij})'],'k--','linewidth',2);
a=axis;
delete(htmp);
tmp=[];
for ir=1:length(itmp),
for it=1:length(endo_prior_restrictions.irf{itmp(ir),3})
temp_index = find(time_matrix{ij}==endo_prior_restrictions.irf{itmp(ir),3}(it));
tmp(temp_index,:) = endo_prior_restrictions.irf{itmp(ir),4};
end
end
% tmp = cell2mat(endo_prior_restrictions.irf(itmp,4));
tmp(isinf(tmp(:,1)),1)=a(3);
tmp(isinf(tmp(:,2)),2)=a(4);
hp = patch([time_matrix{ij} time_matrix{ij}(end:-1:1)],[tmp(:,1); tmp(end:-1:1,2)],'c');
set(hp,'FaceColor',[0.7 0.8 1])
hold on,
plot(time_matrix{ij},[max(irf_matrix{ij})' min(irf_matrix{ij})'],'k--','linewidth',2)
plot(time_matrix{ij},irf_median{ij},'k','linewidth',2)
plot(time_matrix{ij},[irf_distrib{ij}],'k-')
plot(a(1:2),[0 0],'r')
hold off,
axis([max(1,a(1)) a(2:4)])
box on,
set(gca,'xtick',sort(time_matrix{ij}))
itmp = min(itmp);
title([endo_prior_restrictions.irf{itmp,1},' vs ',endo_prior_restrictions.irf{itmp,2}],'interpreter','none'),
end
if any(iplot_indx.*plot_indx==ij),
% MCF of the couples with logical AND
itmp = min(find(plot_indx==ij));
indx1 = find(indx_irf_matrix(:,ij)==0);
indx2 = find(indx_irf_matrix(:,ij)~=0);
leg = num2str(time_matrix{ij}(1));
leg = [leg '...' num2str(time_matrix{ij}(end))];
aleg = 'ALL';
atitle0=[endo_prior_restrictions.irf{itmp,1},' vs ',endo_prior_restrictions.irf{itmp,2}, '(', leg,')'];
fprintf(['%4.1f%% of the ',type,' support matches IRF restrictions ',atitle0,'\n'],length(indx1)/length(irestrictions)*100)
% aname=[type '_irf_calib_',int2str(ij)];
aname=[type '_irf_calib_',endo_prior_restrictions.irf{itmp,1},'_vs_',endo_prior_restrictions.irf{itmp,2},'_',aleg];
atitle=[type ' IRF Calib: Parameter(s) driving ',endo_prior_restrictions.irf{itmp,1},' vs ',endo_prior_restrictions.irf{itmp,2}, '(', leg,')'];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'IRF restriction';
options_mcf.nobeha_title = 'NO IRF restriction';
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, DynareOptions);
end
end
% tmp = cell2mat(endo_prior_restrictions.irf(itmp,4));
tmp(isinf(tmp(:,1)),1)=a(3);
tmp(isinf(tmp(:,2)),2)=a(4);
hp = patch([time_matrix{ij} time_matrix{ij}(end:-1:1)],tmp(:),'b');
set(hp,'FaceAlpha',[0.5])
plot(a(1:2),[0 0],'r')
hold off,
axis([max(1,a(1)) a(2:4)])
box on,
set(gca,'xtick',sort(time_matrix{ij}))
itmp = min(itmp);
title([endo_prior_restrictions.irf{itmp,1},' vs ',endo_prior_restrictions.irf{itmp,2}],'interpreter','none'),
end
end
dyn_saveas(h1,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],DynareOptions);
if ~DynareOptions.nograph,
dyn_saveas(h1,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],DynareOptions);
end
skipline()
end
if ~isempty(indx_moment)
skipline()
disp('Deleting old MOMENT calibration plots ...')
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_calib*.eps']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_calib*.fig']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_calib*.pdf']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions.eps']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions.fig']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
a=dir([OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions.pdf']);
for j=1:length(a),
delete([OutputDirectoryName,filesep,a(j).name]);
end
disp('done !')
skipline()
options_mcf.param_names = char(BayesInfo.name);
all_moment_couples = cellstr([char(endo_prior_restrictions.moment(:,1)) char(endo_prior_restrictions.moment(:,2))]);
moment_couples = unique(all_moment_couples);
nbr_moment_couples = size(moment_couples,1);
plot_indx = NaN(nbr_moment_couples,1);
time_matrix=cell(nbr_moment_couples,1);
indx_moment_matrix=zeros(length(irestrictions),nbr_moment_couples);
moment_matrix=cell(nbr_moment_couples,1);
moment_mean=cell(nbr_moment_couples,1);
moment_median=cell(nbr_moment_couples,1);
moment_var=cell(nbr_moment_couples,1);
moment_HPD=cell(nbr_moment_couples,1);
moment_distrib=cell(nbr_moment_couples,1);
% For single legend search which has maximum nbr of restrictions
% For single legend search which has maximum nbr of restrictions
maxijv=0;
for ij=1:nbr_moment_restrictions
if length(endo_prior_restrictions.moment{ij,3})>maxijv
@ -262,18 +360,22 @@ if ~isempty(indx_moment)
plot_indx(ij) = find(strcmp(moment_couples,all_moment_couples(ij,:)));
time_matrix{plot_indx(ij)} = [time_matrix{plot_indx(ij)} endo_prior_restrictions.moment{ij,3}];
end
iplot_indx = ones(size(plot_indx));
indx_moment = indx_moment(irestrictions,:);
h2=dyn_figure(DynareOptions,'name',[type ' evaluation of moment restrictions']);
nrow=ceil(sqrt(nbr_moment_couples));
ncol=nrow;
if nrow*(nrow-1)>nbr_moment_couples,
ncol=nrow-1;
if ~DynareOptions.nograph,
h2=dyn_figure(DynareOptions,'name',[type ' evaluation of moment restrictions']);
nrow=ceil(sqrt(nbr_moment_couples));
ncol=nrow;
if nrow*(nrow-1)>nbr_moment_couples,
ncol=nrow-1;
end
end
for ij=1:nbr_moment_restrictions,
mat_moment{ij}=mat_moment{ij}(irestrictions,:);
moment_matrix{plot_indx(ij)} = [moment_matrix{plot_indx(ij)} mat_moment{ij}];
indx_moment_matrix(:,plot_indx(ij)) = indx_moment_matrix(:,plot_indx(ij)) + indx_moment(:,ij);
for ik=1:size(mat_moment{ij},2),
[Mean,Median,Var,HPD,Distrib] = ...
posterior_moments(mat_moment{ij}(:,ik),0,DynareOptions.mh_conf_sig);
@ -288,82 +390,113 @@ if ~isempty(indx_moment)
if size(mat_moment{ij},2)>1,
leg = [leg,':' ,num2str(endo_prior_restrictions.moment{ij,3}(end))];
aleg = [aleg,'_' ,num2str(endo_prior_restrictions.moment{ij,3}(end))];
iplot_indx(ij)=0;
end
if length(time_matrix{plot_indx(ij)})==1,
figure(h2),
if ~DynareOptions.nograph && length(time_matrix{plot_indx(ij)})==1,
set(0,'currentfigure',h2);
subplot(nrow,ncol,plot_indx(ij)),
hc = cumplot(mat_moment{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold all,
a=axis; delete(hc),
% hist(mat_moment{ij}),
a=axis;
x1val=max(endo_prior_restrictions.moment{ij,4}(1),a(1));
x2val=min(endo_prior_restrictions.moment{ij,4}(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceAlpha', 0.5)
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(mat_moment{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold off,
title([endo_prior_restrictions.moment{ij,1},' vs ',endo_prior_restrictions.moment{ij,2},'(',leg,')'],'interpreter','none'),
% if ij==maxij
% leg1 = num2str(endo_prior_restrictions.moment{ij,3}(:));
% [legend_h,object_h,plot_h,text_strings]=legend(leg1);
% Position=get(legend_h,'Position');Position(1:2)=[-0.055 0.95-Position(4)];
% set(legend_h,'Position',Position);
% end
% if ij==maxij
% leg1 = num2str(endo_prior_restrictions.moment{ij,3}(:));
% [legend_h,object_h,plot_h,text_strings]=legend(leg1);
% Position=get(legend_h,'Position');Position(1:2)=[-0.055 0.95-Position(4)];
% set(legend_h,'Position',Position);
% end
end
indx1 = find(indx_moment(:,ij)==0);
indx2 = find(indx_moment(:,ij)~=0);
atitle0=[endo_prior_restrictions.moment{ij,1},' vs ',endo_prior_restrictions.moment{ij,2}, '(', leg,')'];
fprintf(['%4.1f%% of the prior support matches MOMENT ',atitle0,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,endo_prior_restrictions.moment{ij,4})
fprintf(['%4.1f%% of the ',type,' support matches MOMENT ',atitle0,' inside [%4.1f, %4.1f]\n'],length(indx1)/length(irestrictions)*100,endo_prior_restrictions.moment{ij,4})
% aname=[type '_moment_calib_',int2str(ij)];
aname=[type '_moment_calib_',endo_prior_restrictions.moment{ij,1},'_vs_',endo_prior_restrictions.moment{ij,2},'_',aleg];
atitle=[type ' MOMENT Calib: Parameter(s) driving ',endo_prior_restrictions.moment{ij,1},' vs ',endo_prior_restrictions.moment{ij,2}, '(', leg,')'];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'moment prior restriction';
options_mcf.nobeha_title = 'NO moment prior restriction';
options_mcf.beha_title = 'moment restriction';
options_mcf.nobeha_title = 'NO moment restriction';
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat, indx1, indx2, options_mcf, DynareOptions);
end
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
% stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
% end
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
% stab_map_1(xmat, indx1, indx2, aname, 1, indplot, OutputDirectoryName,[],atitle);
% end
end
figure(h2);
for ij=1:nbr_moment_couples,
if length(time_matrix{ij})>1,
subplot(nrow,ncol, ij)
itmp = (find(plot_indx==ij));
plot(time_matrix{ij},[max(moment_matrix{ij})' min(moment_matrix{ij})'],'k--','linewidth',2)
hold on,
plot(time_matrix{ij},moment_median{ij},'k','linewidth',2)
plot(time_matrix{ij},[moment_distrib{ij}],'k-')
a=axis;
tmp=[];
for ir=1:length(itmp),
for it=1:length(endo_prior_restrictions.moment{itmp(ir),3})
temp_index = find(time_matrix{ij}==endo_prior_restrictions.moment{itmp(ir),3}(it));
tmp(temp_index,:) = endo_prior_restrictions.moment{itmp(ir),4};
if ~DynareOptions.nograph
itmp = (find(plot_indx==ij));
set(0,'currentfigure',h2);
subplot(nrow,ncol, ij)
htmp = plot(time_matrix{ij},[max(moment_matrix{ij})' min(moment_matrix{ij})'],'k--','linewidth',2);
a=axis;
delete(htmp);
tmp=[];
for ir=1:length(itmp),
for it=1:length(endo_prior_restrictions.moment{itmp(ir),3})
temp_index = find(time_matrix{ij}==endo_prior_restrictions.moment{itmp(ir),3}(it));
tmp(temp_index,:) = endo_prior_restrictions.moment{itmp(ir),4};
end
end
% tmp = cell2mat(endo_prior_restrictions.moment(itmp,4));
tmp(isinf(tmp(:,1)),1)=a(3);
tmp(isinf(tmp(:,2)),2)=a(4);
hp = patch([time_matrix{ij} time_matrix{ij}(end:-1:1)],[tmp(:,1); tmp(end:-1:1,2)],'b');
set(hp,'FaceColor',[0.7 0.8 1])
hold on,
plot(time_matrix{ij},[max(moment_matrix{ij})' min(moment_matrix{ij})'],'k--','linewidth',2)
plot(time_matrix{ij},moment_median{ij},'k','linewidth',2)
plot(time_matrix{ij},[moment_distrib{ij}],'k-')
plot(a(1:2),[0 0],'r')
hold off,
axis(a)
box on,
set(gca,'xtick',sort(time_matrix{ij}))
itmp = min(itmp);
title([endo_prior_restrictions.moment{itmp,1},' vs ',endo_prior_restrictions.moment{itmp,2}],'interpreter','none'),
end
if any(iplot_indx.*plot_indx==ij),
% MCF of the couples with logical AND
itmp = min(find(plot_indx==ij));
indx1 = find(indx_moment_matrix(:,ij)==0);
indx2 = find(indx_moment_matrix(:,ij)~=0);
leg = num2str(time_matrix{ij}(1));
leg = [leg '...' num2str(time_matrix{ij}(end))];
aleg = 'ALL';
atitle0=[endo_prior_restrictions.moment{itmp,1},' vs ',endo_prior_restrictions.moment{itmp,2}, '(', leg,')'];
fprintf(['%4.1f%% of the ',type,' support matches MOMENT restrictions ',atitle0,'\n'],length(indx1)/length(irestrictions)*100)
% aname=[type '_moment_calib_',int2str(ij)];
aname=[type '_moment_calib_',endo_prior_restrictions.moment{itmp,1},'_vs_',endo_prior_restrictions.moment{itmp,2},'_',aleg];
atitle=[type ' MOMENT Calib: Parameter(s) driving ',endo_prior_restrictions.moment{itmp,1},' vs ',endo_prior_restrictions.moment{itmp,2}, '(', leg,')'];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'moment restriction';
options_mcf.nobeha_title = 'NO moment restriction';
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat, indx1, indx2, options_mcf, DynareOptions);
end
end
% tmp = cell2mat(endo_prior_restrictions.moment(itmp,4));
tmp(isinf(tmp(:,1)),1)=a(3);
tmp(isinf(tmp(:,2)),2)=a(4);
hp = patch([time_matrix{ij} time_matrix{ij}(end:-1:1)],tmp(:),'b');
set(hp,'FaceAlpha',[0.5])
plot(a(1:2),[0 0],'r')
hold off,
axis(a)
box on,
set(gca,'xtick',sort(time_matrix{ij}))
itmp = min(itmp);
title([endo_prior_restrictions.moment{itmp,1},' vs ',endo_prior_restrictions.moment{itmp,2}],'interpreter','none'),
end
end
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],DynareOptions);
if ~DynareOptions.nograph,
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],DynareOptions);
end
skipline()
end
return

4
matlab/gsa/mcf_analysis.m
View File

@ -1,4 +1,4 @@
function mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
@ -53,7 +53,7 @@ if length(ibeha)>10 && length(inobeha)>10,
indcorr = indcorr(~ismember(indcorr(:),indmcf));
indmcf = [indmcf(:); indcorr(:)];
end
if ~isempty(indmcf)
if ~isempty(indmcf) && ~DynareOptions.nograph,
skipline()
scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names(indmcf,:), ...
'.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,[], DynareOptions, ...

492
matlab/gsa/redform_map.m
View File

@ -53,21 +53,31 @@ alpha2 = options_gsa_.alpha2_redform;
alpha2=0;
pvalue_ks = options_gsa_.ksstat_redform;
pvalue_corr = options_gsa_.alpha2_redform;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
fname_ = M_.fname;
bounds = prior_bounds(bayestopt_,options_);
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
dirname='';
end
if pprior
load([dirname,filesep,M_.fname,'_prior'],'lpmat', 'lpmat0', 'istable','T');
adir=[dirname filesep 'redform_stab'];
adir=[dirname filesep 'redform_prior'];
type = 'prior';
else
load([dirname,filesep,M_.fname,'_mc'],'lpmat', 'lpmat0', 'istable','T');
adir=[dirname filesep 'redform_mc'];
type = 'mc';
end
options_mcf.pvalue_ks = options_gsa_.ksstat_redform;
options_mcf.pvalue_corr = options_gsa_.alpha2_redform;
options_mcf.alpha2 = options_gsa_.alpha2_redform;
options_mcf.param_names = pnames;
options_mcf.fname_ = M_.fname;
options_mcf.OutputDirectoryName = adir;
if ~exist('T')
stab_map_(dirname,options_gsa_);
if pprior
@ -106,6 +116,15 @@ else
pd = [bayestopt_.p6(offset+1:end) bayestopt_.p7(offset+1:end) bayestopt_.p3(offset+1:end) bayestopt_.p4(offset+1:end)];
end
options_map.param_names = pnames;
options_map.fname_ = M_.fname;
options_map.OutputDirectoryName = adir;
options_map.iload = iload;
options_map.log_trans = ilog;
options_map.prior_range = options_gsa_.prior_range;
options_map.pshape = pshape;
options_map.pd = pd;
nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
lpmat=[];
lpmat0=[];
@ -119,7 +138,7 @@ for j=1:size(anamendo,1)
namexo=deblank(anamexo(jx,:));
iexo=strmatch(namexo,M_.exo_names,'exact');
skipline()
disp(['[', namendo,' vs. ',namexo,']'])
disp(['[', namendo,' vs ',namexo,']'])
if ~isempty(iexo),
@ -128,7 +147,7 @@ for j=1:size(anamendo,1)
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0 && isempty(threshold) && ~options_.nograph,
ifig=ifig+1;
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ', namendo,' vs. shocks ',int2str(ifig)]);
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ', namendo,' vs shocks ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
@ -139,7 +158,15 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir0))
mkdir(xdir0)
end
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namexo, xdir0, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namexo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = [namendo,' vs ', namexo];
options_map.OutputDirectoryName = xdir0;
si(:,js) = redform_private(x0, y0, options_map, options_);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
@ -148,41 +175,65 @@ for j=1:size(anamendo,1)
mkdir(xdir)
end
if ~options_.nograph,
hf=dyn_figure(options_,'name',['Reduced Form Mapping: ',namendo,' vs. ', namexo]); hist(y0,30), title([namendo,' vs. ', namexo],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, namendo,'_vs_', namexo],options_);
hf=dyn_figure(options_,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs ', namexo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off,
title([namendo,' vs ', namexo ' - threshold [' num2str(threshold(1)) ' ' num2str(threshold(2)) ']'],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namexo],options_);
end
% if ~isempty(iy),
% si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namexo, xdir, options_gsa_);
% else
si(:,js) = NaN(np,1);
% end
if length(iy)>size(x0,2) && length(iyc)>size(x0,2)
delete([xdir, '/*threshold*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'threshold',0);
% indsmirnov = find(dproba>ksstat);
indsmirnov = find(proba<pvalue_ks);
for jp=1:length(indsmirnov),
disp([M_.param_names(estim_params_.param_vals(indsmirnov(jp),1),:),' d-stat = ', num2str(dproba(indsmirnov(jp)),'%1.3f'),' p-value = ', num2str(proba(indsmirnov(jp)),'%1.3f')])
end
skipline()
stab_map_1(x0, iy, iyc, 'threshold',pvalue_ks,indsmirnov,xdir,[],['Reduced Form Mapping (Threshold) for ', namendo,' vs. lagged ', namexo]);
stab_map_2(x0(iy,:),alpha2,pvalue_corr,'inside_threshold',xdir,[],['Reduced Form Mapping (Inside Threshold)for ', namendo,' vs. lagged ', namexo])
stab_map_2(x0(iyc,:),alpha2,pvalue_corr,'outside_threshold',xdir,[],['Reduced Form Mapping (Outside Threshold) for ', namendo,' vs. lagged ', namexo])
delete([xdir, '/*threshold*.*'])
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namexo];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'inside threshold';
options_mcf.nobeha_title = 'outside threshold';
options_mcf.title = atitle0;
options_mcf.OutputDirectoryName = xdir;
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
lpmat=x0(iy,:);
if nshocks,
lpmat0=xx0(iy,:);
end
istable=[1:length(iy)];
save([xdir,filesep,'threshold.mat'],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
save([xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namexo '_threshold' ],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
lpmat=[]; lpmat0=[]; istable=[];
else
icheck=[];
end
if isempty(icheck),
atitle0=['Monte Carlo Filtering for ',namendo,' vs ', namexo];
options_mcf.title = atitle0;
indmcf = redform_mcf(y0, x0, options_mcf, options_);
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
if isempty(dir(xdir))
mkdir(xdir)
end
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namexo, xdir, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for log-transformed ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namexo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = ['log(' namendo ' vs ' namexo ')'];
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
if isempty(threshold) && ~options_.nograph,
@ -203,7 +254,7 @@ for j=1:size(anamendo,1)
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namexo],'interpreter','none')
title([logflag,' ',namendo,' vs ',namexo],'interpreter','none')
if iplo==9,
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)],options_);
end
@ -221,7 +272,7 @@ for j=1:size(anamendo,1)
namlagendo=deblank(anamlagendo(je,:));
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
skipline()
disp(['[', namendo,' vs. lagged ',namlagendo,']'])
disp(['[', namendo,' vs lagged ',namlagendo,']'])
if ~isempty(ilagendo),
%y0=squeeze(T(iendo,ilagendo,istable));
@ -229,7 +280,7 @@ for j=1:size(anamendo,1)
if (max(y0)-min(y0))>1.e-10,
if mod(iplo,9)==0 && isempty(threshold) && ~options_.nograph,
ifig=ifig+1;
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ' namendo,' vs. lags ',int2str(ifig)]);
hfig = dyn_figure(options_,'name',['Reduced Form Mapping: ' namendo,' vs lags ',int2str(ifig)]);
iplo=0;
end
iplo=iplo+1;
@ -240,7 +291,15 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir0))
mkdir(xdir0)
end
si(:,js) = redform_private(x0, y0, pshape, pd, iload, pnames, namendo, namlagendo, xdir0, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = [namendo,' vs ', namlagendo];
options_map.OutputDirectoryName = xdir0;
si(:,js) = redform_private(x0, y0, options_map, options_);
else
iy=find( (y0>threshold(1)) & (y0<threshold(2)));
iyc=find( (y0<=threshold(1)) | (y0>=threshold(2)));
@ -248,41 +307,67 @@ for j=1:size(anamendo,1)
if isempty(dir(xdir))
mkdir(xdir)
end
% if ~isempty(iy)
% si(:,js) = redform_private(x0(iy,:), y0(iy), pshape, pd, iload, pnames, namendo, namlagendo, xdir, options_gsa_);
% end
if ~options_.nograph,
hf=dyn_figure(options_,'name',['Reduced Form Mapping: ',namendo,' vs. lagged ', namlagendo]); hist(y0,30), title([namendo,' vs. lagged ', namlagendo],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, namendo,'_vs_', namlagendo],options_);
hf=dyn_figure(options_,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs lagged ', namlagendo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off,
title([namendo,' vs lagged ', namlagendo ' - threshold [' num2str(threshold(1)) ' ' num2str(threshold(2)) ']'],'interpreter','none')
dyn_saveas(hf,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo],options_);
end
if length(iy)>size(x0,2) && length(iyc)>size(x0,2),
delete([xdir, '/*threshold*.*'])
[proba, dproba] = stab_map_1(x0, iy, iyc, 'threshold',0);
% indsmirnov = find(dproba>ksstat);
indsmirnov = find(proba<pvalue_ks);
for jp=1:length(indsmirnov),
disp([M_.param_names(estim_params_.param_vals(indsmirnov(jp),1),:),' d-stat = ', num2str(dproba(indsmirnov(jp)),'%1.3f'),' p-value = ', num2str(proba(indsmirnov(jp)),'%1.3f')])
end
skipline()
stab_map_1(x0, iy, iyc, 'threshold',pvalue_ks,indsmirnov,xdir,[],['Reduced Form Mapping (Threshold) for ', namendo,' vs. lagged ', namlagendo]);
stab_map_2(x0(iy,:),alpha2,pvalue_corr,'inside_threshold',xdir,[],['Reduced Form Mapping (Inside Threshold) for ', namendo,' vs. lagged ', namlagendo])
stab_map_2(x0(iyc,:),alpha2,pvalue_corr,'outside_threshold',xdir,[],['Reduced Form Mapping (Outside Threshold) for ', namendo,' vs. lagged ', namlagendo])
delete([xdir, '/*threshold*.*'])
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_mcf.amcf_name = aname;
options_mcf.amcf_title = atitle;
options_mcf.beha_title = 'inside threshold';
options_mcf.nobeha_title = 'outside threshold';
options_mcf.title = atitle0;
options_mcf.OutputDirectoryName = xdir;
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
lpmat=x0(iy,:);
if nshocks,
lpmat0=xx0(iy,:);
end
istable=[1:length(iy)];
save([xdir,filesep,'threshold.mat'],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
save([xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo '_threshold' ],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
lpmat=[]; lpmat0=[]; istable=[];
else
icheck = [];
end
if isempty(icheck),
atitle0=['Monte Carlo Filtering for ',namendo,' vs ', namlagendo];
options_mcf.title = atitle0;
indmcf = redform_mcf(y0, x0, options_mcf, options_);
end
end
else
[yy, xdir] = log_trans_(y0,xdir0);
if isempty(dir(xdir))
mkdir(xdir)
end
silog(:,js) = redform_private(x0, yy, pshape, pd, iload, pnames, namendo, namlagendo, xdir, options_gsa_);
atitle0=['Reduced Form Mapping (ANOVA) for log-transformed ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo];
atitle=[type ' Reduced Form Mapping (ANOVA): Parameter(s) driving ',namendo,' vs ',namlagendo];
options_map.amap_name = aname;
options_map.amap_title = atitle;
options_map.figtitle = atitle0;
options_map.title = ['log(' namendo ' vs ' namlagendo ')'];
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
if isempty(threshold) && ~options_.nograph
@ -303,7 +388,7 @@ for j=1:size(anamendo,1)
for ip=1:min(np,10),
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
title([logflag,' ',namendo,' vs ',namlagendo,'(-1)'],'interpreter','none')
if iplo==9,
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)],options_);
end
@ -351,13 +436,17 @@ if isempty(threshold) && ~options_.nograph,
end
end
function si = redform_private(x0, y0, pshape, pd, iload, pnames, namy, namx, xdir, opt_gsa)
global bayestopt_ options_
function si = redform_private(x0, y0, options_map, options_)
% opt_gsa=options_.opt_gsa;
np=size(x0,2);
x00=x0;
if opt_gsa.prior_range,
ilog = options_map.log_trans;
iload = options_map.iload;
pnames = options_map.param_names;
pd = options_map.pd;
pshape = options_map.pshape;
xdir = options_map.OutputDirectoryName;
if options_map.prior_range,
for j=1:np,
x0(:,j)=(x0(:,j)-pd(j,3))./(pd(j,4)-pd(j,3));
end
@ -365,61 +454,286 @@ else
x0=priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
end
fname=[xdir,'/map'];
if ilog,
fname=[xdir filesep options_map.fname_ '_' options_map.amap_name '_log'];
else
fname=[xdir filesep options_map.fname_ '_' options_map.amap_name];
end
if iload==0,
if isempty(dir(xdir))
mkdir(xdir)
end
if ~options_.nograph,
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ', namy,' vs. ', namx]); hist(y0,30), title([namy,' vs. ', namx],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx],options_);
end
% gsa_ = gsa_sdp_dyn(y0, x0, -2, [],[],[],1,fname, pnames);
nrun=length(y0);
nest=min(250,nrun);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
if nfit>nest,
gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
% gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
[ys,is] = sort(y0);
istep = ceil(nrun/nest);
iest = is(floor(istep/2):istep:end);
nest = length(iest);
irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
istep = ceil(length(irest)/(nfit-nest));
ifit = union(iest, irest(1:istep:end));
if ~ismember(irest(end),ifit),
ifit = union(ifit, irest(end));
end
nfit=length(ifit);
% ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
% ifit = iest;
% nfit=nest;
ipred = setdiff([1:nrun],ifit);
if ilog,
[y1, tmp, isig, lam] = log_trans_(y0(iest));
y1 = log(y0*isig+lam);
end
save([fname,'.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(1:nfit,:);
if ~options_.nograph,
hfig=gsa_sdp_plot(gsa_,fname,pnames,iii(1:min(12,np)));
if options_.nodisplay
close(hfig);
hfig=dyn_figure(options_,'name',options_map.figtitle);
subplot(221)
if ilog,
hist(y1,30),
else
hist(y0,30),
end
title(options_map.title,'interpreter','none')
subplot(222)
if ilog,
hc = cumplot(y1);
else
hc = cumplot(y0);
end
set(hc,'color','k','linewidth',2)
title([options_map.title ' CDF'],'interpreter','none')
end
gsa_.x0=x0(1:nfit,:);
gsa0 = ss_anova(y0(iest), x0(iest,:), 1);
if ilog,
[gsa22, gsa1, gsax] = ss_anova_log(y1(iest), x0(iest,:), isig, lam, gsa0);
end
% if (gsa1.out.bic-gsa0.out.bic) < 10,
% y00=y0;
% gsa00=gsa0;
% gsa0=gsa1;
% y0=y1;
% ilog=1;
% end
if nfit>nest,
% gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
nvr = gsa0.nvr*nest^3/nfit^3;
nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
if ilog
gsa0 = gsa_;
nvr1 = gsa1.nvr*nest^3/nfit^3;
nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
nvrx = gsax.nvr*nest^3/nfit^3;
nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
[gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
% gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
% gsa2=gsa1;
% gsa2.y = gsa0.y;
% gsa2.fit = (exp(gsa1.fit)-lam)*isig;
% gsa2.f0 = mean(gsa2.fit);
% gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
% gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
% gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
% for j=1:np,
% gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
% gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
% gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
% end
% nvr = gsax.nvr*nest^3/nfit^3;
% nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
% gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
% gsa22=gsa2;
% gsa22.fit = gsa2.fit+gsax.fit;
% gsa22.f0 = mean(gsa22.fit);
% gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
% gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
% gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
% for j=1:np,
% gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
% gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
% gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
% end
gsa_ = gsa22;
end
else
if ilog
gsa_ = gsa22;
else
gsa_ = gsa0;
end
end
save([fname,'_map.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(ifit,:);
if ~options_.nograph,
hmap=gsa_sdp_plot(gsa_,[fname '_map'],pnames,iii(1:min(12,np)));
set(hmap,'name',options_map.amap_title);
end
gsa_.x0=x0(ifit,:);
% copyfile([fname,'_est.mat'],[fname,'.mat'])
if ~options_.nograph,
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ' namy,'_vs_', namx,'_fit']);
plot(y0(1:nfit),[gsa_.fit y0(1:nfit)],'.'),
title([namy,' vs. ', namx,' fit'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_fit'],options_);
figure(hfig);
subplot(223),
plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
r2 = gsa_.r2;
% if ilog,
% plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
% r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
% else
% plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
% r2 = gsa_.r2;
% end
title(['Learning sample fit - R2=' num2str(r2,2)],'interpreter','none')
if nfit<nrun,
npred=[nfit+1:nrun];
yf = ss_anova_fcast(x0(npred,:), gsa_);
hfig=dyn_figure(options_,'name',['Reduced Form Mapping: ' namy,'_vs_', namx,'_pred']);
plot(y0(npred),[yf y0(npred)],'.'),
title([namy,' vs. ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_pred'],options_);
if ilog,
yf = ss_anova_fcast(x0(ipred,:), gsa1);
yf = log_trans_(yf,'',isig,lam)+ss_anova_fcast(x0(ipred,:), gsax);
else
yf = ss_anova_fcast(x0(ipred,:), gsa_);
end
yn = y0(ipred);
r2 = 1-cov(yf-yn)/cov(yn);
subplot(224),
plot(yn,[yf yn],'.'),
title(['Out-of-sample prediction - R2=' num2str(r2,2)],'interpreter','none')
end
dyn_saveas(hfig,fname,options_);
if options_.nodisplay
close(hmap);
end
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
% gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
load([fname,'_map.mat'],'gsa_')
if ~options_.nograph,
yf = ss_anova_fcast(x0, gsa_);
hfig=dyn_figure(options_,['Reduced Form Mapping: ' namy,'_vs_', namx,'_pred']);
hfig=dyn_figure(options_,'name',options_map.title);
plot(y0,[yf y0],'.'),
title([namy,' vs. ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[xdir,filesep, namy,'_vs_', namx,'_pred'],options_);
title([namy,' vs ', namx,' pred'],'interpreter','none')
dyn_saveas(hfig,[fname '_pred'],options_);
end
end
% si = gsa_.multivariate.si;
si = gsa_.si;
return
function gsa2 = log2level_map(gsa1, isig, lam)
nest=length(gsa1.y);
np = size(gsa1.x0,2);
gsa2=gsa1;
gsa2.y = log_trans_(gsa1.y,'',isig,lam);
gsa2.fit = (exp(gsa1.fit)-lam)*isig;
gsa2.f0 = mean(gsa2.fit);
gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
for j=1:np,
gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
gsa2.fses(:,j) = exp(gsa1.fs(:,j)+gsa1.fses(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0-gsa2.fs(:,j);
gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
end
return
function [gsa22, gsa1, gsax] = ss_anova_log(y,x,isig,lam,gsa0,nvrs)
[nest, np]=size(x);
if nargin==6,
gsa1 = ss_anova(y, x, 1, 0, 2, nvrs(:,1));
else
gsa1 = ss_anova(y, x, 1);
end
gsa2 = log2level_map(gsa1, isig, lam);
if nargin >=5 && ~isempty(gsa0),
for j=1:np,
nvr2(j) = var(diff(gsa2.fs(:,j),2));
nvr0(j) = var(diff(gsa0.fs(:,j),2));
end
inda = find((gsa0.stat<2)&(gsa1.stat>2));
inda = inda(log10(nvr0(inda)./nvr2(inda))/2<0);
gsa1.nvr(inda)=gsa1.nvr(inda).*10.^(log10(nvr0(inda)./nvr2(inda)));
gsa1 = ss_anova(y, x, 1, 0, 2, gsa1.nvr);
gsa2 = log2level_map(gsa1, isig, lam);
end
if nargin==6,
gsax = ss_anova(gsa2.y-gsa2.fit, x, 1, 0, 2, nvrs(:,2));
else
gsax = ss_anova(gsa2.y-gsa2.fit, x, 1);
end
gsa22=gsa2;
gsa22.fit = gsa2.fit+gsax.fit;
gsa22.f0 = mean(gsa22.fit);
gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
for j=1:np,
gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
gsa22.fses(:,j) = gsax.fses(:,j);
gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
end
return
function indmcf = redform_mcf(y0, x0, options_mcf, options_)
hfig=dyn_figure(options_,'name',options_mcf.amcf_title);
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
density] = posterior_moments(y0,1,0.9);
post_deciles = [-inf; post_deciles; inf];
for jt=1:10,
indy{jt}=find( (y0>post_deciles(jt)) & (y0<=post_deciles(jt+1)));
leg{jt}=[int2str(jt) '-dec'];
end
[proba, dproba] = stab_map_1(x0, indy{1}, indy{end}, [],0);
indmcf=find(proba<options_mcf.pvalue_ks);
[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
indmcf = indmcf(jtmp);
nbr_par = length(indmcf);
nrow=ceil(sqrt(nbr_par+1));
ncol=nrow;
if nrow*(nrow-1)>nbr_par,
ncol=nrow-1;
end
cmap = colormap(jet(10));
for jx=1:nbr_par,
subplot(nrow,ncol,jx)
hold off
for jt=1:10,
h=cumplot(x0(indy{jt},indmcf(jx)));
set(h,'color', cmap(jt,:), 'linewidth', 2)
hold all,
end
title(options_mcf.param_names(indmcf(jx),:),'interpreter','none')
end
hleg = legend(leg);
aa=get(hleg,'Position');
aa(1)=1-aa(3)-0.02;
aa(2)=0.02;
set(hleg,'Position',aa);
if ~isoctave
annotation('textbox', [0.25,0.01,0.5,0.05], ...
'String', options_mcf.title, ...
'Color','black',...
'FontWeight','bold',...
'interpreter','none',...
'horizontalalignment','center');
end
dyn_saveas(hfig,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],options_);
return

4
matlab/gsa/scatter_mcf.m
View File

@ -158,8 +158,8 @@ for i = 1:p
end
end
if ~isoctave
annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center');
annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center');
annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center','interpreter','none');
annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center','interpreter','none');
end
if ~nograph,

16
matlab/gsa/stab_map_.m
View File

@ -267,7 +267,11 @@ if fload==0,
M_ = set_all_parameters([lpmat0(j,:) lpmat(j,:)]',estim_params_,M_);
%try stoch_simul([]);
try
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
if ~ isempty(options_.endogenous_prior_restrictions.moment)
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
else
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
end
infox(j,1)=info(1);
if infox(j,1)==0 && ~exist('T'),
dr_=oo_.dr;
@ -557,7 +561,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'unique Stable Saddle-Path';
options_mcf.nobeha_title = 'NO unique Stable Saddle-Path';
options_mcf.title = 'unique solution';
mcf_analysis(lpmat, istable, itmp, options_mcf, options_)
mcf_analysis(lpmat, istable, itmp, options_mcf, options_);
if ~isempty(iindeterm),
itmp = itot(find(~ismember(itot,iindeterm)));
@ -566,7 +570,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO indeterminacy';
options_mcf.nobeha_title = 'indeterminacy';
options_mcf.title = 'indeterminacy';
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, options_)
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, options_);
end
if ~isempty(ixun),
@ -576,7 +580,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO explosive solution';
options_mcf.nobeha_title = 'explosive solution';
options_mcf.title = 'instability';
mcf_analysis(lpmat, itmp, ixun, options_mcf, options_)
mcf_analysis(lpmat, itmp, ixun, options_mcf, options_);
end
inorestriction = istable(find(~ismember(istable,irestriction))); % what went wrong beyong prior restrictions
@ -588,7 +592,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'NO inability to find a solution';
options_mcf.nobeha_title = 'inability to find a solution';
options_mcf.title = 'inability to find a solution';
mcf_analysis(lpmat, itmp, iwrong, options_mcf, options_)
mcf_analysis(lpmat, itmp, iwrong, options_mcf, options_);
end
if ~isempty(irestriction),
@ -598,7 +602,7 @@ if length(iunstable)>0 || length(iwrong)>0,
options_mcf.beha_title = 'prior IRF/moment calibration';
options_mcf.nobeha_title = 'NO prior IRF/moment calibration';
options_mcf.title = 'prior restrictions';
mcf_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, options_)
mcf_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, options_);
iok = irestriction(1);
x0 = [lpmat0(iok,:)'; lpmat(iok,:)'];
else

3
matlab/identification_analysis.m
View File

@ -85,6 +85,9 @@ if info(1)==0,
derivatives_info.DYss=dYss;
if init,
indJJ = (find(max(abs(JJ'),[],1)>1.e-8));
if isempty(indJJ) && any(any(isnan(JJ)))
error('There are NaN in the JJ matrix. Please check whether your model has units roots and you forgot to set lik_init~=1.' )
end
while length(indJJ)<nparam && nlags<10,
disp('The number of moments with non-zero derivative is smaller than the number of parameters')
disp(['Try increasing ar = ', int2str(nlags+1)])

2
matlab/initial_estimation_checks.m
View File

@ -49,7 +49,7 @@ old_steady_params=Model.params; %save initial parameters for check if steady sta
% % check if steady state solves static model (except if diffuse_filter == 1)
[DynareResults.steady_state, new_steady_params] = evaluate_steady_state(DynareResults.steady_state,Model,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
if ~isempty(EstimatedParameters.param_vals)
if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.param_vals)
%check whether steady state file changes estimated parameters
Model_par_varied=Model; %store Model structure
Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters

2
matlab/lmmcp/lmmcp.m
View File

@ -392,7 +392,7 @@ while (k < kmax) && (Psix > eps2)
Fx = Fxnew;
Phix = Phixnew;
Psix = Psixnew;
[~,DFx] = feval(FUN,x,varargin{:});
[junk,DFx] = feval(FUN,x,varargin{:});
DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
DPsix = DPhix'*Phix;
normDPsix = norm(DPsix);

6
matlab/maximize_prior_density.m
View File

@ -1,4 +1,4 @@
function [xparams,lpd,hessian] = ...
function [xparams,lpd,hessian_mat] = ...
maximize_prior_density(iparams, prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound,DynareOptions,DynareModel,BayesInfo,EstimatedParams,DynareResults)
% Maximizes the logged prior density using Chris Sims' optimization routine.
%
@ -13,7 +13,7 @@ function [xparams,lpd,hessian] = ...
% OUTPUTS
% xparams [double] vector, prior mode.
% lpd [double] scalar, value of the logged prior density at the mode.
% hessian [double] matrix, Hessian matrix at the prior mode.
% hessian_mat [double] matrix, Hessian matrix at the prior mode.
% Copyright (C) 2009-2015 Dynare Team
%
@ -32,7 +32,7 @@ function [xparams,lpd,hessian] = ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[xparams, lpd, exitflag, hessian]=dynare_minimize_objective('minus_logged_prior_density', ...
[xparams, lpd, exitflag, hessian_mat]=dynare_minimize_objective('minus_logged_prior_density', ...
iparams, DynareOptions.mode_compute, DynareOptions, [prior_inf_bound, prior_sup_bound], ...
BayesInfo.name, BayesInfo, [], ...
prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound, ...

41
matlab/metropolis_hastings_initialization.m
View File

@ -1,7 +1,7 @@
function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
%function [ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] =
% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_,dataset_info,,options_,M_,estim_params_,bayestopt_,oo_)
%function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Metropolis-Hastings initialization.
%
% INPUTS
@ -11,6 +11,7 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck,
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
@ -18,12 +19,25 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck,
% o oo_ outputs structure
%
% OUTPUTS
% None
% o ix2 [double] (nblck*npar) vector of starting points for different chains
% o ilogpo2 [double] (nblck*1) vector of initial posterior values for different chains
% o ModelName [string] name of the mod-file
% o MetropolisFolder [string] path to the Metropolis subfolder
% o fblck [scalar] number of the first MH chain to be run (not equal to 1 in case of recovery)
% o fline [double] (nblck*1) vector of first draw in each chain (not equal to 1 in case of recovery)
% o npar [scalar] number of parameters estimated
% o nblck [scalar] Number of MCM chains requested
% o nruns [double] (nblck*1) number of draws in each chain
% o NewFile [scalar] (nblck*1) vector storing the number
% of the first MH-file to created for each chain when saving additional
% draws
% o MAX_nruns [scalar] maximum number of draws in each MH-file on the harddisk
% o d [double] (p*p) matrix, Cholesky decomposition of the posterior covariance matrix (at the mode).
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2013 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -40,6 +54,7 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
%Initialize outputs
ix2 = [];
ilogpo2 = [];
ModelName = [];
@ -68,7 +83,7 @@ MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
d = chol(vv);
if ~options_.load_mh_file && ~options_.mh_recover
% Here we start a new metropolis-hastings, previous draws are discarded.
% Here we start a new Metropolis-Hastings, previous draws are discarded.
if nblck > 1
disp('Estimation::mcmc: Multiple chains mode.')
else
@ -80,7 +95,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
delete([BaseName '_mh*_blck*.mat']);
disp('Estimation::mcmc: Old mh-files successfully erased!')
end
% Delete old metropolis log file.
% Delete old Metropolis log file.
file = dir([ MetropolisFolder '/metropolis.log']);
if length(file)
delete([ MetropolisFolder '/metropolis.log']);
@ -128,11 +143,11 @@ if ~options_.load_mh_file && ~options_.mh_recover
if init_iter > 100 && validate == 0
disp(['Estimation::mcmc: I couldn''t get a valid initial value in 100 trials.'])
if options_.nointeractive
disp(['Estimation::mcmc: I reduce mh_init_scale by ten percent:'])
disp(['Estimation::mcmc: I reduce mh_init_scale by 10 percent:'])
options_.mh_init_scale = .9*options_.mh_init_scale;
disp(sprintf('Estimation::mcmc: Parameter mh_init_scale is now equal to %f.',options_.mh_init_scale))
else
disp(['Estimation::mcmc: You should Reduce mh_init_scale...'])
disp(['Estimation::mcmc: You should reduce mh_init_scale...'])
disp(sprintf('Estimation::mcmc: Parameter mh_init_scale is equal to %f.',options_.mh_init_scale))
options_.mh_init_scale = input('Estimation::mcmc: Enter a new value... ');
end
@ -217,7 +232,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
fclose(fidlog);
elseif options_.load_mh_file && ~options_.mh_recover
% Here we consider previous mh files (previous mh did not crash).
disp('Estimation::mcmc: I am loading past metropolis-hastings simulations...')
disp('Estimation::mcmc: I am loading past Metropolis-Hastings simulations...')
load_last_mh_history_file(MetropolisFolder, ModelName);
mh_files = dir([ MetropolisFolder filesep ModelName '_mh*.mat']);
if ~length(mh_files)
@ -238,7 +253,7 @@ elseif options_.load_mh_file && ~options_.mh_recover
nblck = past_number_of_blocks;
options_.mh_nblck = nblck;
end
% I read the last line of the last mh-file for initialization of the new metropolis-hastings simulations:
% I read the last line of the last mh-file for initialization of the new Metropolis-Hastings simulations:
LastFileNumber = record.LastFileNumber;
LastLineNumber = record.LastLineNumber;
if LastLineNumber < MAX_nruns
@ -252,7 +267,7 @@ elseif options_.load_mh_file && ~options_.mh_recover
ix2 = record.LastParameters;
fblck = 1;
NumberOfPreviousSimulations = sum(record.MhDraws(:,1),1);
fprintf('Estimation::mcmc: I am writting a new mh-history file... ');
fprintf('Estimation::mcmc: I am writing a new mh-history file... ');
record.MhDraws = [record.MhDraws;zeros(1,3)];
NumberOfDrawsWrittenInThePastLastFile = MAX_nruns - LastLineNumber;
NumberOfDrawsToBeSaved = nruns(1) - NumberOfDrawsWrittenInThePastLastFile;
@ -318,7 +333,7 @@ elseif options_.mh_recover
disp('Estimation::mcmc: It appears that you don''t need to use the mh_recover option!')
disp(' You have to edit the mod file and remove the mh_recover option')
disp(' in the estimation command')
error()
error('Estimation::mcmc: mh_recover option not required!')
end
% I count the number of saved mh files per block.
NumberOfMhFilesPerBlock = zeros(nblck,1);
@ -339,7 +354,7 @@ elseif options_.mh_recover
end
% How many mh-files are saved in this block?
NumberOfSavedMhFilesInTheCrashedBlck = NumberOfMhFilesPerBlock(fblck);
% Correct the number of saved mh files if the crashed metropolis was not the first session (so
% Correct the number of saved mh files if the crashed Metropolis was not the first session (so
% that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
% of the current session).
if OldMh

12
matlab/mode_check.m
View File

@ -1,8 +1,8 @@
function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
function mode_check(fun,x,hessian_mat,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Checks the estimated ML mode or Posterior mode.
%@info:
%! @deftypefn {Function File} mode_check (@var{fun}, @var{x}, @var{hessian}, @var{DynareDataset}, @var{DynareOptions}, @var{Model}, @var{EstimatedParameters}, @var{BayesInfo}, @var{DynareResults})
%! @deftypefn {Function File} mode_check (@var{fun}, @var{x}, @var{hessian_mat}, @var{DynareDataset}, @var{DynareOptions}, @var{Model}, @var{EstimatedParameters}, @var{BayesInfo}, @var{DynareResults})
%! @anchor{mode_check}
%! @sp 1
%! Checks the estimated ML mode or Posterior mode by plotting sections of the likelihood/posterior kernel.
@ -58,17 +58,17 @@ function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
TeX = DynareOptions.TeX;
if ~isempty(hessian);
[ s_min, k ] = min(diag(hessian));
if ~isempty(hessian_mat);
[ s_min, k ] = min(diag(hessian_mat));
end
fval = feval(fun,x,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
if ~isempty(hessian);
if ~isempty(hessian_mat);
skipline()
disp('MODE CHECK')
skipline()
disp(sprintf('Fval obtained by the minimization routine: %f', fval))
fprintf('Fval obtained by the minimization routine (minus the posterior/likelihood)): %f', fval);
skipline()
if s_min<eps
disp(sprintf('Most negative variance %f for parameter %d (%s = %f)', s_min, k , BayesInfo.name{k}, x(k)))

2
matlab/modules/dseries

@ -1 +1 @@
Subproject commit c976d14444eeea21bc2cb36770344c0456e94e79
Subproject commit 55686d7f06673bde2ab52e75833a1bbdba16b2a1

2
matlab/modules/reporting

@ -1 +1 @@
Subproject commit d242c92493693a8dc3b369f36b8762d8c31179fd
Subproject commit b7d5391051635207f088a493c92d7357f3bdd4e9

18
matlab/non_linear_dsge_likelihood.m
View File

@ -319,9 +319,11 @@ DynareOptions.warning_for_steadystate = 0;
LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,start,DynareOptions);
set_dynare_random_generator_state(s1,s2);
if imag(LIK)
info = 46;
likelihood = objective_function_penalty_base;
exit_flag = 0;
elseif isnan(LIK)
info = 45;
likelihood = objective_function_penalty_base;
exit_flag = 0;
else
@ -332,4 +334,18 @@ DynareOptions.warning_for_steadystate = 1;
% Adds prior if necessary
% ------------------------------------------------------------------------------
lnprior = priordens(xparam1(:),BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
fval = (likelihood-lnprior);
fval = (likelihood-lnprior);
if isnan(fval)
info = 47;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if imag(fval)~=0
info = 48;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end

73
matlab/optimization/apprgrdn.m Normal file
View File

@ -0,0 +1,73 @@
function g = apprgrdn(x,f,fun,deltax,obj,varargin)
% g = apprgrdn(x,f,fun,deltax,obj,varargin)
% Performs the finite difference approximation of the gradient <g> at a
% point <x> used in solveopt
%
% Inputs:
% x: point at which to evaluate gradient
% f: calculated function value at a point x;
% fun: Name of the Matlab function calculating the function values
% deltax: vector of the relative stepsizes,
% obj flag indicating whether the gradient of the objective
% function (1) or the constraint function (0) is to be calculated.
%
% Modified by Giovanni Lombardo and Johannes Pfeifer to accomodate Dynare
% structure
%
%
% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
% Copyright (C) 2008-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
n=max(size(x)); ee=ones(size(x));
di=abs(x); idx=find(di<5e-15); di(idx)=5e-15*ee(idx);
di=deltax.*di;
if obj
idx=find(abs(di)<2e-10);
di(idx)=2e-10*sign(di(idx));
else
idx=find(abs(di)<5e-15);
di(idx)=5e-15*sign(di(idx));
end
y=x;
g=NaN(n,1);
for i=1:n
y(i)=x(i)+di(i);
fi=feval(fun,y,varargin{:});
if obj
if fi==f,
for j=1:3
di(i)=di(i)*10; y(i)=x(i)+di(i);
fi=feval(fun,y,varargin{:});
if fi~=f
break
end
end
end
end
g(i)=(fi-f)/di(i);
if obj
if ~isempty(idx) && any(idx==i)
y(i)=x(i)-di(i);
fi=feval(fun,y,varargin{:});
g(i)=.5*(g(i)+(f-fi)/di(i));
end
end
y(i)=x(i);
end

32
matlab/optimization/csminit1.m
View File

@ -1,10 +1,24 @@
function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,varargin)
% [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,...
% P1,P2,P3,P4,P5,P6,P7,P8)
% retcodes: 0, normal step. 5, largest step still improves too fast.
% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
% stepsize still improves too slow. 6, no improvement found. 1, zero
% gradient.
% [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,varargin)
%
% Inputs:
% fcn: [string] string naming the objective function to be minimized
% x0: [npar by 1] initial value of the parameter vector
% g0: [npar by 1] initial value of the gradient vector
% H0: [npar by npar] initial value for the inverse Hessian. Must be positive definite.
% varargin: Optional additional inputs that get handed off to fcn each
% time it is called.
% Outputs:
% fhat: [scalar] function value at minimum
% xhat: [npar by 1] parameter vector at minimum
% fcount [scalar] function iteration count upon termination
% retcode [scalar] 0: normal step
% 1: zero gradient.
% 5: largest step still improves too fast.
% 2,4: back and forth adjustment of stepsize didn't finish.
% 3: smallest stepsize still improves too slow
% 6: no improvement found
%---------------------
% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
% Places where the number of P's need to be altered or the code could be returned to
@ -15,12 +29,12 @@ function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,f0,g0,badg,H0,varargin)
%
% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
% it's not psd.
%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/csminit.m
%
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2008-2011 Dynare Team
% Copyright (C) 2008-2015 Dynare Team
%
% This file is part of Dynare.
%

241
matlab/optimization/csminwel1.m
View File

@ -1,29 +1,48 @@
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
% fcn: string naming the objective function to be minimized
% x0: initial value of the parameter vector
% H0: initial value for the inverse Hessian. Must be positive definite.
% grad: Either a string naming a function that calculates the gradient, or the null matrix.
% If it's null, the program calculates a numerical gradient. In this case fcn must
% be written so that it can take a matrix argument and produce a row vector of values.
% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
% function value by more than crit.
% nit: Maximum number of iterations.
% method: integer scalar, 2, 3 or 5 points formula.
% epsilon: scalar double, numerical differentiation increment
% varargin: A list of optional length of additional parameters that get handed off to fcn each
% time it is called.
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
% Inputs:
% fcn: [string] string naming the objective function to be minimized
% x0: [npar by 1] initial value of the parameter vector
% H0: [npar by npar] initial value for the inverse Hessian. Must be positive definite.
% grad: [string or empty matrix] Either a string naming a function that calculates the gradient, or the null matrix.
% If it's null, the program calculates a numerical gradient. In this case fcn must
% be written so that it can take a matrix argument and produce a row vector of values.
% crit: [scalar] Convergence criterion. Iteration will cease when it proves impossible to improve the
% function value by more than crit.
% nit: [scalar] Maximum number of iterations.
% method: [scalar] integer scalar for selecting gradient method: 2, 3 or 5 points formula.
% epsilon: [scalar] scalar double, numerical differentiation increment
% varargin: Optional additional inputs that get handed off to fcn each
% time it is called.
%
% Note that if the program ends abnormally, it is possible to retrieve the current x,
% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
% may be a decent starting point. One can also start from the one with best function value.)
% writes g2.mat and g3.mat as well. If all were written at about the same time, any of them
% may be a decent starting point. One can also start from the one with best function value.)
%
% Outputs:
% fh: [scalar] function value at minimum
% xh: [npar by 1] parameter vector at minimum
% gh [npar by 1] gradient vector
% H [npar by npar] inverse of the Hessian matrix
% itct [scalar] iteration count upon termination
% fcount [scalar] function iteration count upon termination
% retcodeh [scalar] return code:
% 0: normal step
% 1: zero gradient
% 2: back and forth on step length never finished
% 3: smallest step still improving too slow
% 4: back and forth on step length never finished
% 5: largest step still improving too fast
% 6: smallest step still improving too slow, reversed gradient
% 7: warning: possible inaccuracy in H matrix
%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
%
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2006-2012 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -51,7 +70,6 @@ NumGrad= isempty(grad);
done=0;
itct=0;
fcount=0;
snit=100;
gh = [];
H = [];
retcodeh = [];
@ -74,20 +92,7 @@ if ~cost_flag
end
if NumGrad
switch method
case 2
[g,badg] = numgrad2(fcn, f0, x0, epsilon, varargin{:});
case 3
[g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
case 5
[g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
case 13
[g,badg] = numgrad3_(fcn, f0, x0, epsilon, varargin{:});
case 15
[g,badg] = numgrad5_(fcn, f0, x0, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
[g, badg]=get_num_grad(method,fcn,f0,x0,epsilon,varargin{:});
elseif ischar(grad)
[g,badg] = feval(grad,x0,varargin{:});
else
@ -105,19 +110,15 @@ while ~done
g1=[]; g2=[]; g3=[];
%addition fj. 7/6/94 for control
disp('-----------------')
disp('-----------------')
%disp('f and x at the beginning of new iteration')
disp(sprintf('f at the beginning of new iteration, %20.10f',f))
if Verbose
disp('-----------------')
disp(sprintf('f at the beginning of new iteration, %20.10f',f))
end
%-----------Comment out this line if the x vector is long----------------
% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);
%-------------------------
itct=itct+1;
[f1 x1 fc retcode1] = csminit1(fcn,x,f,g,badg,H,varargin{:});
%ARGLIST
%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
% P8,P9,P10,P11,P12,P13);
% itct=itct+1;
[f1, x1, fc, retcode1] = csminit1(fcn,x,f,g,badg,H,varargin{:});
fcount = fcount+fc;
% erased on 8/4/94
% if (retcode == 1) || (abs(f1-f) < crit)
@ -132,22 +133,9 @@ while ~done
wall1=1; badg1=1;
else
if NumGrad
switch method
case 2
[g1 badg1] = numgrad2(fcn, f1, x1, epsilon, varargin{:});
case 3
[g1 badg1] = numgrad3(fcn, f1, x1, epsilon, varargin{:});
case 5
[g1,badg1] = numgrad5(fcn, f1, x1, epsilon, varargin{:});
case 13
[g1,badg1] = numgrad3_(fcn, f1, x1, epsilon, varargin{:});
case 15
[g1,badg1] = numgrad5_(fcn, f1, x1, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
[g1, badg1]=get_num_grad(method,fcn,f1,x1,epsilon,varargin{:});
elseif ischar(grad),
[g1 badg1] = feval(grad,x1,varargin{:});
[g1, badg1] = feval(grad,x1,varargin{:});
else
[junk1,g1,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg1 = ~cost_flag;
@ -155,8 +143,6 @@ while ~done
wall1=badg1;
% g1
save g1.mat g1 x1 f1 varargin;
%ARGLIST
%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
if wall1 % && (~done) by Jinill
% Bad gradient or back and forth on step length. Possibly at
@ -164,33 +150,19 @@ while ~done
%
%fcliff=fh;xcliff=xh;
Hcliff=H+diag(diag(H).*rand(nx,1));
disp('Cliff. Perturbing search direction.')
[f2 x2 fc retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,varargin{:});
%ARGLIST
%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
% P5,P6,P7,P8,P9,P10,P11,P12,P13);
if Verbose
disp('Cliff. Perturbing search direction.')
end
[f2, x2, fc, retcode2] = csminit1(fcn,x,f,g,badg,Hcliff,varargin{:});
fcount = fcount+fc; % put by Jinill
if f2 < f
if retcode2==2 || retcode2==4
wall2=1; badg2=1;
else
if NumGrad
switch method
case 2
[g2 badg2] = numgrad2(fcn, f2, x2, epsilon, varargin{:});
case 3
[g2 badg2] = numgrad3(fcn, f2, x2, epsilon, varargin{:});
case 5
[g2,badg2] = numgrad5(fcn, f2, x2, epsilon, varargin{:});
case 13
[g2,badg2] = numgrad3_(fcn, f2, x2, epsilon, varargin{:});
case 15
[g2,badg2] = numgrad5_(fcn, f2, x2, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
[g2, badg2]=get_num_grad(method,fcn,f2,x2,epsilon,varargin{:});
elseif ischar(grad),
[g2 badg2] = feval(grad,x2,varargin{:});
[g2, badg2] = feval(grad,x2,varargin{:});
else
[junk1,g2,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg2 = ~cost_flag;
@ -199,8 +171,6 @@ while ~done
% g2
badg2
save g2.mat g2 x2 f2 varargin
%ARGLIST
%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
if wall2
disp('Cliff again. Try traversing')
@ -208,33 +178,19 @@ while ~done
f3=f; x3=x; badg3=1;retcode3=101;
else
gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
if(size(x0,2)>1), gcliff=gcliff', end
[f3 x3 fc retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),varargin{:});
%ARGLIST
%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
% P4,P5,P6,P7,P8,...
% P9,P10,P11,P12,P13);
if(size(x0,2)>1)
gcliff=gcliff';
end
[f3, x3, fc, retcode3] = csminit1(fcn,x,f,gcliff,0,eye(nx),varargin{:});
fcount = fcount+fc; % put by Jinill
if retcode3==2 || retcode3==4
wall3=1; badg3=1;
wall3=1;
badg3=1;
else
if NumGrad
switch method
case 2
[g3 badg3] = numgrad2(fcn, f3, x3, epsilon, varargin{:});
case 3
[g3 badg3] = numgrad3(fcn, f3, x3, epsilon, varargin{:});
case 5
[g3,badg3] = numgrad5(fcn, f3, x3, epsilon, varargin{:});
case 13
[g3,badg3] = numgrad3_(fcn, f3, x3, epsilon, varargin{:});
case 15
[g3,badg3] = numgrad5_(fcn, f3, x3, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
[g3, badg3]=get_num_grad(method,fcn,f3,x3,epsilon,varargin{:});
elseif ischar(grad),
[g3 badg3] = feval(grad,x3,varargin{:});
[g3, badg3] = feval(grad,x3,varargin{:});
else
[junk1,g3,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badg3 = ~cost_flag;
@ -242,8 +198,6 @@ while ~done
wall3=badg3;
% g3
save g3.mat g3 x3 f3 varargin;
%ARGLIST
%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
end
else
@ -292,22 +246,9 @@ while ~done
end
if nogh
if NumGrad
switch method
case 2
[gh,badgh] = numgrad2(fcn, fh, xh, epsilon, varargin{:});
case 3
[gh,badgh] = numgrad3(fcn, fh, xh, epsilon, varargin{:});
case 5
[gh,badgh] = numgrad5(fcn, fh, xh, epsilon, varargin{:});
case 13
[gh,badgh] = numgrad3_(fcn, fh, xh, epsilon, varargin{:});
case 15
[gh,badgh] = numgrad5_(fcn, fh, xh, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
[gh, badgh]=get_num_grad(method,fcn,fh,xh,epsilon,varargin{:});
elseif ischar(grad),
[gh badgh] = feval(grad, xh,varargin{:});
[gh, badgh] = feval(grad, xh,varargin{:});
else
[junk1,gh,junk2, cost_flag] = feval(fcn,x1,varargin{:});
badgh = ~cost_flag;
@ -316,11 +257,6 @@ while ~done
badgh=1;
end
%end of picking
%ih
%fh
%xh
%gh
%badgh
stuck = (abs(fh-f) < crit);
if (~badg) && (~badgh) && (~stuck)
H = bfgsi1(H,gh-g,xh-x);
@ -338,24 +274,47 @@ while ~done
done = 1;
end
rc=retcodeh;
if rc == 1
disp('zero gradient')
elseif rc == 6
disp('smallest step still improving too slow, reversed gradient')
elseif rc == 5
disp('largest step still improving too fast')
elseif (rc == 4) || (rc==2)
disp('back and forth on step length never finished')
elseif rc == 3
disp('smallest step still improving too slow')
elseif rc == 7
disp('warning: possible inaccuracy in H matrix')
if Verbose || done
if rc ==0
%do nothing, just a normal step
elseif rc == 1
disp('zero gradient')
elseif rc == 6
disp('smallest step still improving too slow, reversed gradient')
elseif rc == 5
disp('largest step still improving too fast')
elseif (rc == 4) || (rc==2)
disp('back and forth on step length never finished')
elseif rc == 3
disp('smallest step still improving too slow')
elseif rc == 7
disp('warning: possible inaccuracy in H matrix')
else
error('Unaccounted Case, please contact the developers')
end
end
% end
f=fh;
x=xh;
g=gh;
badg=badgh;
end
% what about making an m-file of 10 lines including numgrad.m
% since it appears three times in csminwel.m
end
function [g, badg]=get_num_grad(method,fcn,f0,x0,epsilon,varargin)
switch method
case 2
[g,badg] = numgrad2(fcn, f0, x0, epsilon, varargin{:});
case 3
[g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
case 5
[g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
case 13
[g,badg] = numgrad3_(fcn, f0, x0, epsilon, varargin{:});
case 15
[g,badg] = numgrad5_(fcn, f0, x0, epsilon, varargin{:});
otherwise
error('csminwel1: Unknown method for gradient evaluation!')
end
end

148
matlab/optimization/dynare_minimize_objective.m
View File

@ -46,17 +46,13 @@ function [opt_par_values,fval,exitflag,hessian_mat,options_,Scale]=dynare_minimi
%% set bounds and parameter names if not already set
n_params=size(start_par_value,1);
if isempty(bounds)
if minimizer_algorithm==10
if isnumeric(minimizer_algorithm) && minimizer_algorithm==10
error('Algorithm 10 (simpsa) requires upper and lower bounds')
else
bounds=[-Inf(n_params,1) Inf(n_params,1)];
end
end
if minimizer_algorithm==10 && any(any(isinf(bounds)))
error('Algorithm 10 (simpsa) requires finite upper and lower bounds')
end
if isempty(parameter_names)
parameter_names=[repmat('parameter ',n_params,1),num2str((1:n_params)')];
end
@ -87,7 +83,51 @@ switch minimizer_algorithm
[opt_par_values,fval,exitflag,output,lamdba,grad,hessian_mat] = ...
fmincon(objective_function,start_par_value,[],[],[],[],bounds(:,1),bounds(:,2),[],optim_options,varargin{:});
case 2
error('Optimization algorithm 1 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
%simulating annealing
sa_options = options_.saopt;
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'neps'
sa_options.neps = options_list{i,2};
case 'rt'
sa_options.rt = options_list{i,2};
case 'MaxIter'
sa_options.MaxIter = options_list{i,2};
case 'TolFun'
sa_options.TolFun = options_list{i,2};
case 'verbosity'
sa_options.verbosity = options_list{i,2};
case 'initial_temperature'
sa_options.initial_temperature = options_list{i,2};
case 'ns'
sa_options.ns = options_list{i,2};
case 'nt'
sa_options.nt = options_list{i,2};
case 'step_length_c'
sa_options.step_length_c = options_list{i,2};
case 'initial_step_length'
sa_options.initial_step_length = options_list{i,2};
otherwise
warning(['solveopt: Unknown option (' options_list{i,1} ')!'])
end
end
end
npar=length(start_par_value);
[LB, UB]=set_bounds_to_finite_values(bounds, options_.huge_number);
fprintf('\nNumber of parameters= %d, initial temperatur= %4.3f \n', npar,sa_options.initial_temperature);
fprintf('rt= %4.3f; TolFun= %4.3f; ns= %4.3f;\n',sa_options.rt,sa_options.TolFun,sa_options.ns);
fprintf('nt= %4.3f; neps= %4.3f; MaxIter= %d\n',sa_options.nt,sa_options.neps,sa_options.MaxIter);
fprintf('Initial step length(vm): %4.3f; step_length_c: %4.3f\n', sa_options.initial_step_length,sa_options.step_length_c);
fprintf('%-20s %-6s %-6s %-6s\n','Name:', 'LB;','Start;','UB;');
for pariter=1:npar
fprintf('%-20s %6.4f; %6.4f; %6.4f;\n',parameter_names{pariter}, LB(pariter),start_par_value(pariter),UB(pariter));
end
sa_options.initial_step_length= sa_options.initial_step_length*ones(npar,1); %bring step length to correct vector size
sa_options.step_length_c= sa_options.step_length_c*ones(npar,1); %bring step_length_c to correct vector size
[opt_par_values, fval,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_draws, t, vm] =...
simulated_annealing(objective_function,start_par_value,sa_options,LB,UB,varargin{:});
case 3
if isoctave && ~user_has_octave_forge_package('optim')
error('Optimization algorithm 3 requires the optim package')
@ -143,8 +183,9 @@ switch minimizer_algorithm
analytic_grad=[];
end
% Call csminwell.
[fval,opt_par_values,grad,hessian_mat,itct,fcount,exitflag] = ...
[fval,opt_par_values,grad,inverse_hessian_mat,itct,fcount,exitflag] = ...
csminwel1(objective_function, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, varargin{:});
hessian_mat=inv(inverse_hessian_mat);
case 5
if options_.analytic_derivation==-1 %set outside as code for use of analytic derivation
analytic_grad=1;
@ -192,7 +233,13 @@ switch minimizer_algorithm
if ~isempty(options_.optim_opt)
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
[opt_par_values,fval,exitflag] = fminsearch(objective_function,start_par_value,optim_options,varargin{:});
if ~isoctave
[opt_par_values,fval,exitflag] = fminsearch(objective_function,start_par_value,optim_options,varargin{:});
else
% Under Octave, use a wrapper, since fminsearch() does not have a 4th arg
func = @(x) objective_function(x,varargin{:});
[opt_par_values,fval,exitflag] = fminsearch(func,start_par_value,optim_options);
end
case 8
% Dynare implementation of the simplex algorithm.
simplexOptions = options_.simplex;
@ -273,50 +320,48 @@ switch minimizer_algorithm
end
simpsaOptionsList = options2cell(simpsaOptions);
simpsaOptions = simpsaset(simpsaOptionsList{:});
[opt_par_values, fval, exitflag] = simpsa(func2str(objective_function),start_par_value,bounds(:,1),bounds(:,2),simpsaOptions,varargin{:});
[LB, UB]=set_bounds_to_finite_values(bounds, options_.huge_number);
[opt_par_values, fval, exitflag] = simpsa(func2str(objective_function),start_par_value,LB,UB,simpsaOptions,varargin{:});
case 11
options_.cova_compute = 0 ;
[opt_par_values,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(start_par_value,varargin{:}) ;
case 101
myoptions=soptions;
myoptions(2)=1e-6; %accuracy of argument
myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
myoptions(5)= 1.0;
[opt_par_values,fval]=solvopt(start_par_value,objective_function,[],myoptions,varargin{:});
solveoptoptions = options_.solveopt;
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'TolX'
solveoptoptions.TolX = options_list{i,2};
case 'TolFun'
solveoptoptions.TolFun = options_list{i,2};
case 'MaxIter'
solveoptoptions.MaxIter = options_list{i,2};
case 'verbosity'
solveoptoptions.verbosity = options_list{i,2};
case 'SpaceDilation'
solveoptoptions.SpaceDilation = options_list{i,2};
case 'LBGradientStep'
solveoptoptions.LBGradientStep = options_list{i,2};
otherwise
warning(['solveopt: Unknown option (' options_list{i,1} ')!'])
end
end
end
[opt_par_values,fval]=solvopt(start_par_value,objective_function,[],[],[],solveoptoptions,varargin{:});
case 102
%simulating annealing
LB = start_par_value - 1;
UB = start_par_value + 1;
neps=10;
% Set input parameters.
maxy=0;
epsilon=1.0e-9;
rt_=.10;
t=15.0;
ns=10;
nt=10;
maxevl=100000000;
idisp =1;
npar=length(start_par_value);
disp(['size of param',num2str(length(start_par_value))])
c=.1*ones(npar,1);
%* Set input values of the input/output parameters.*
vm=1*ones(npar,1);
disp(['number of parameters= ' num2str(npar) 'max= ' num2str(maxy) 't= ' num2str(t)]);
disp(['rt_= ' num2str(rt_) 'eps= ' num2str(epsilon) 'ns= ' num2str(ns)]);
disp(['nt= ' num2str(nt) 'neps= ' num2str(neps) 'maxevl= ' num2str(maxevl)]);
disp ' ';
disp ' ';
disp(['starting values(x) ' num2str(start_par_value')]);
disp(['initial step length(vm) ' num2str(vm')]);
disp(['lower bound(lb)', 'initial conditions', 'upper bound(ub)' ]);
disp([LB start_par_value UB]);
disp(['c vector ' num2str(c')]);
[opt_par_values, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparstart_par_valueam1,maxy,rt_,epsilon,ns,nt ...
,neps,maxevl,LB,UB,c,idisp ,t,vm,varargin{:});
if isoctave
error('Optimization algorithm 2 is not available under Octave')
elseif ~user_has_matlab_license('GADS_Toolbox')
error('Optimization algorithm 2 requires the Global Optimization Toolbox')
end
% Set default optimization options for fmincon.
optim_options = saoptimset('display','iter','TolFun',1e-8);
if ~isempty(options_.optim_opt)
eval(['optim_options = saoptimset(optim_options,' options_.optim_opt ');']);
end
func = @(x)objective_function(x,varargin{:});
[opt_par_values,fval,exitflag,output] = simulannealbnd(func,start_par_value,bounds(:,1),bounds(:,2),optim_options);
otherwise
if ischar(minimizer_algorithm)
if exist(options_.mode_compute)
@ -328,3 +373,12 @@ switch minimizer_algorithm
error(['Optimization algorithm ' int2str(minimizer_algorithm) ' is unknown!'])
end
end
end
function [LB, UB]=set_bounds_to_finite_values(bounds, huge_number)
LB=bounds(:,1);
LB(isinf(LB))=-huge_number;
UB=bounds(:,2);
UB(isinf(UB))=huge_number;
end

2
matlab/optimization/gmhmaxlik.m
View File

@ -20,7 +20,7 @@ function [PostMode, HessianMatrix, Scale, ModeValue] = gmhmaxlik(fun, xinit, Hin
% Set default options
if ~isempty(Hinit);
gmhmaxlikOptionsptions.varinit = 'previous';
gmhmaxlikOptions.varinit = 'previous';
else
gmhmaxlikOptions.varinit = 'prior';
end

459
matlab/optimization/simulated_annealing.m Normal file
View File

@ -0,0 +1,459 @@
function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_draws, t, vm] = ...
simulated_annealing(fcn,x,optim,lb,ub,varargin)
% function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_draws, t, vm] = ...
% simulated_annealing(fcn,x,optim,lb,ub,varargin)
%
% Implements the continuous simulated annealing global optimization
% algorithm described in Corana et al. (1987)
%
% A very quick (perhaps too quick) overview of SA:
% SA tries to find the global optimum of an N dimensional function.
% It moves both up and downhill and as the optimization process
% proceeds, it focuses on the most promising area.
% To start, it randomly chooses a trial point within the step length
% VM (a vector of length N) of the user selected starting point. The
% function is evaluated at this trial point and its value is compared
% to its value at the initial point.
% In a maximization problem, all uphill moves are accepted and the
% algorithm continues from that trial point. Downhill moves may be
% accepted the decision is made by the Metropolis criteria. It uses T
% (temperature) and the size of the downhill move in a probabilistic
% manner. The smaller T and the size of the downhill move are, the more
% likely that move will be accepted. If the trial is accepted, the
% algorithm moves on from that point. If it is rejected, another point
% is chosen instead for a trial evaluation.
% Each element of VM periodically adjusted so that half of all
% function evaluations in that direction are accepted.
% A fall in T is imposed upon the system with the RT option by
% T(i+1) = RT*T(i) where i is the ith iteration. Thus, as T declines,
% downhill moves are less likely to be accepted and the percentage of
% rejections rise. Given the scheme for the selection for VM, VM falls.
% Thus, as T declines, VM falls and SA focuses upon the most promising
% area for optimization.
%
% The importance of the parameter T (initial_temperature):
% The parameter T is crucial in using SA successfully. It influences
% VM, the step length over which the algorithm searches for optima. For
% a small intial T, the step length may be too small thus not enough
% of the function might be evaluated to find the global optima. The user
% should carefully examine VM in the intermediate output (set verbosity =
% 1) to make sure that VM is appropriate. The relationship between the
% initial temperature and the resulting step length is function
% dependent.
% To determine the starting temperature that is consistent with
% optimizing a function, it is worthwhile to run a trial run first. Set
% RT = 1.5 and T = 1.0. With RT > 1.0, the temperature increases and VM
% rises as well. Then select the T that produces a large enough VM.
%
% Input Parameters:
% Note: The suggested values generally come from Corana et al. To
% drastically reduce runtime, see Goffe et al., pp. 90-1 for
% suggestions on choosing the appropriate RT and NT.
%
% fcn - function to be optimized.
% x - The starting values for the variables of the function to be
% optimized. (N)
% optim: Options structure with fields
%
% optim.maximizer_indicator - Denotes whether the function should be maximized or
% minimized. A value =1 denotes maximization while a
% value =0 denotes minimization. Intermediate output (see verbosity)
% takes this into account.
% optim.RT - The temperature reduction factor
% optim.TolFun - Error tolerance for termination. If the final function
% values from the last neps temperatures differ from the
% corresponding value at the current temperature by less than
% optim.TolFun and the final function value at the current temperature
% differs from the current optimal function value by less than
% optim.TolFun, execution terminates and exitflag = 0 is returned.
% optim.ns - Number of cycles. After NS*N function evaluations, each
% element of VM is adjusted so that approximately half of
% all function evaluations are accepted. The suggested value
% is 20.
% optim.nt - Number of iterations before temperature reduction. After
% NT*NS*N function evaluations, temperature (T) is changed
% by the factor optim.RT. Value suggested by Corana et al. is
% max(100, 5*n). See Goffe et al. for further advice.
% optim.neps - Number of final function values used to decide upon termi-
% nation. See optim.TolFun. Suggested value is 4.
% optim.MaxIter - Maximum number of function evaluations. If it is
% exceeded, exitflag = 1.
% optim.step_length_c - Vector that controls the step length adjustment. The suggested
% value for all elements is 2.0.
% optim.verbosity - controls printing inside SA.
% Values: 0 - Nothing printed.
% 1 - Function value for the starting value and summary results before each temperature
% reduction. This includes the optimal function value found so far, the total
% number of moves (broken up into uphill, downhill, accepted and rejected), the
% number of out of bounds trials, the number of new optima found at this
% temperature, the current optimal X and the step length VM. Note that there are
% N*NS*NT function evalutations before each temperature reduction. Finally, notice is
% is also given upon achieveing the termination criteria.
% 2 - Each new step length (VM), the current optimal X (XOPT) and the current trial X (X). This
% gives the user some idea about how far X strays from XOPT as well as how VM is adapting
% to the function.
% 3 - Each function evaluation, its acceptance or rejection and new optima. For many problems,
% this option will likely require a small tree if hard copy is used. This option is best
% used to learn about the algorithm. A small value for optim.MaxIter is thus recommended when
% using optim.verbosity = 3.
% optim.initial_temperature initial temperature. See Goffe et al. for advice.
% optim.initial_step_length (VM) step length vector. On input it should encompass the
% region of interest given the starting value X. For point
% X(I), the next trial point is selected is from X(I) - VM(I)
% to X(I) + VM(I). Since VM is adjusted so that about half
% of all points are accepted, the input value is not very
% important (i.e. is the value is off, SA adjusts VM to the
% correct value)
%
% lb - The lower bound for the allowable solution variables.
% ub - The upper bound for the allowable solution variables.
% If the algorithm chooses X(I) < LB(I) or X(I) > UB(I),
% I = 1, N, a point is from inside is randomly selected.
% This focuses the algorithm on the region inside UB and LB.
% Unless the user wishes to concentrate the search to a par-
% ticular region, UB and LB should be set to very large positive
% and negative values, respectively. Note that the starting
% vector X should be inside this region. Also note that LB and
% UB are fixed in position, while VM is centered on the last
% accepted trial set of variables that optimizes the function.
%
%
% Input/Output Parameters:
%
% Output Parameters:
% xopt - The variables that optimize the function. (N)
% fopt - The optimal value of the function.
% exitflag - The error return number.
% Values: 0 - Normal return termination criteria achieved.
% 1 - Number of function evaluations (NFCNEV) is
% greater than the maximum number (optim.MaxIter).
% 2 - The starting value (X) is not inside the
% bounds (LB and UB).
% 3 - The initial temperature is not positive.
% 99 - Should not be seen only used internally.
% n_accepted_draws - The number of accepted function evaluations.
% n_total_draws - The total number of function evaluations. In a minor
% point, note that the first evaluation is not used in the
% core of the algorithm it simply initializes the
% algorithm.
% n_out_of_bounds_draws - The total number of trial function evaluations that
% would have been out of bounds of LB and UB. Note that
% a trial point is randomly selected between LB and UB.
% t: On output, the final temperature.
% vm: Final step length vector
%
% Algorithm:
% This routine implements the continuous simulated annealing global
% optimization algorithm described in Corana et al.'s article
% "Minimizing Multimodal Functions of Continuous Variables with the
% "Simulated Annealing" Algorithm" in the September 1987 (vol. 13,
% no. 3, pp. 262-280) issue of the ACM Transactions on Mathematical
% Software.
%
% For modifications to the algorithm and many details on its use,
% (particularly for econometric applications) see Goffe, Ferrier
% and Rogers, "Global Optimization of Statistical Functions with
% Simulated Annealing," Journal of Econometrics, vol. 60, no. 1/2,
% Jan./Feb. 1994, pp. 65-100.
%
% Based on the Matlab code written by Thomas Werner (Bundesbank December
% 2002), which in turn is based on the GAUSS version of Bill Goffe's simulated annealing
% program for global optimization, written by E.G.Tsionas (9/4/95).
%
% Copyright (C) 1995 E.G.Tsionas
% Copyright (C) 1995-2002 Thomas Werner
% Copyright (C) 2002-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
c=optim.step_length_c;
t=optim.initial_temperature;
vm=optim.initial_step_length;
n=size(x,1);
xp=zeros(n,1);
%* Set initial values.*
n_accepted_draws=0;
n_out_of_bounds_draws=0;
n_total_draws=0;
exitflag=99;
xopt=x;
nacp=zeros(n,1);
fstar=1e20*ones(optim.neps,1);
%* If the initial temperature is not positive, notify the user and abort. *
if(t<=0.0);
fprintf('\nThe initial temperature is not positive. Reset the variable t\n');
exitflag=3;
return;
end;
%* If the initial value is out of bounds, notify the user and abort. *
if(sum(x>ub)+sum(x<lb)>0);
fprintf('\nInitial condition out of bounds\n');
exitflag=2;
return;
end;
%* Evaluate the function with input x and return value as f. *
f=feval(fcn,x,varargin{:});
%*
% If the function is to be minimized, switch the sign of the function.
% Note that all intermediate and final output switches the sign back
% to eliminate any possible confusion for the user.
%*
if(optim.maximizer_indicator==0);
f=-f;
end;
n_total_draws=n_total_draws+1;
fopt=f;
fstar(1)=f;
if(optim.verbosity >1);
disp ' ';
disp(['initial x ' num2str(x(:)')]);
if(optim.maximizer_indicator);
disp(['initial f ' num2str(f)]);
else
disp(['initial f ' num2str(-f)]);
end;
end;
% Start the main loop. Note that it terminates if (i) the algorithm
% succesfully optimizes the function or (ii) there are too many
% function evaluations (more than optim.MaxIter).
while (1>0);
nup=0;
nrej=0;
nnew=0;
ndown=0;
lnobds=0;
m=1;
while m<=optim.nt;
j=1;
while j<=optim.ns;
h=1;
while h<=n;
%* Generate xp, the trial value of x. Note use of vm to choose xp. *
i=1;
while i<=n;
if(i==h);
xp(i)=x(i)+(rand(1,1)*2.0-1.0)*vm(i);
else
xp(i)=x(i);
end;
%* If xp is out of bounds, select a point in bounds for the trial. *
if((xp(i)<lb(i) || xp(i)>ub(i)));
xp(i)=lb(i)+(ub(i)-lb(i))*rand(1,1);
lnobds=lnobds+1;
n_out_of_bounds_draws=n_out_of_bounds_draws+1;
if(optim.verbosity >=3);
if exist('fp','var')
print_current_invalid_try(optim.maximizer_indicator,xp,x,fp,f);
end
end;
end;
i=i+1;
end;
%* Evaluate the function with the trial point xp and return as fp. *
% fp=feval(fcn,xp,listarg);
fp=feval(fcn,xp,varargin{:});
if(optim.maximizer_indicator==0);
fp=-fp;
end;
n_total_draws=n_total_draws+1;
if(optim.verbosity >=3);
print_current_valid_try(optim.maximizer_indicator,xp,x,fp,f);
end;
%* If too many function evaluations occur, terminate the algorithm. *
if(n_total_draws>=optim.MaxIter);
fprintf('Too many function evaluations; consider\n');
fprintf('increasing optim.MaxIter or optim.TolFun or decreasing\n');
fprintf('optim.nt or optim.rt. These results are likely to be poor\n');
if(optim.maximizer_indicator==0);
fopt=-fopt;
end;
exitflag=1;
return;
end;
%* Accept the new point if the function value increases. *
if(fp>=f);
if(optim.verbosity >=3);
fprintf('point accepted\n');
end;
x=xp;
f=fp;
n_accepted_draws=n_accepted_draws+1;
nacp(h)=nacp(h)+1;
nup=nup+1;
%* If greater than any other point, record as new optimum. *
if(fp>fopt);
if(optim.verbosity >=3);
fprintf('new optimum\n');
end;
xopt=xp;
fopt=fp;
nnew=nnew+1;
end;
%*
% If the point is lower, use the Metropolis criteria to decide on
% acceptance or rejection.
%*
else
p=exp((fp-f)/t);
pp=rand(1,1);
if(pp<p);
if(optim.verbosity >=3);
if(optim.maximizer_indicator);
fprintf('though lower, point accepted\n');
else
fprintf('though higher, point accepted\n');
end;
end;
x=xp;
f=fp;
n_accepted_draws=n_accepted_draws+1;
nacp(h)=nacp(h)+1;
ndown=ndown+1;
else
nrej=nrej+1;
if(optim.verbosity >=3);
if(optim.maximizer_indicator);
fprintf('lower point rejected\n');
else
fprintf('higher point rejected\n');
end;
end;
end;
end;
h=h+1;
end;
j=j+1;
end;
%* Adjust vm so that approximately half of all evaluations are accepted. *
i=1;
while i<=n;
ratio=nacp(i)/optim.ns;
if(ratio>.6);
vm(i)=vm(i)*(1.+c(i)*(ratio-.6)/.4);
elseif(ratio<.4);
vm(i)=vm(i)/(1.+c(i)*((.4-ratio)/.4));
end;
if(vm(i)>(ub(i)-lb(i)));
vm(i)=ub(i)-lb(i);
end;
i=i+1;
end;
if(optim.verbosity >=2);
fprintf('intermediate results after step length adjustment\n');
fprintf('new step length(vm) %4.3f', vm(:)');
fprintf('current optimal x %4.3f', xopt(:)');
fprintf('current x %4.3f', x(:)');
end;
nacp=zeros(n,1);
m=m+1;
end;
if(optim.verbosity >=1);
print_intermediate_statistics(optim.maximizer_indicator,t,xopt,vm,fopt,nup,ndown,nrej,lnobds,nnew);
end;
%* Check termination criteria. *
quit=0;
fstar(1)=f;
if((fopt-fstar(1))<=optim.TolFun);
quit=1;
end;
if(sum(abs(f-fstar)>optim.TolFun)>0);
quit=0;
end;
%* Terminate SA if appropriate. *
if(quit);
exitflag=0;
if(optim.maximizer_indicator==0);
fopt=-fopt;
end;
if(optim.verbosity >=1);
fprintf('SA achieved termination criteria.exitflag=0\n');
end;
return;
end;
%* If termination criteria are not met, prepare for another loop. *
t=optim.rt*t;
i=optim.neps;
while i>=2;
fstar(i)=fstar(i-1);
i=i-1;
end;
f=fopt;
x=xopt;
%* Loop again. *
end;
end
function print_current_invalid_try(max,xp,x,fp,f)
fprintf('\n');
disp(['Current x ' num2str(x(:)')]);
if(max);
disp(['Current f ' num2str(f)]);
else
disp(['Current f ' num2str(-f)]);
end;
disp(['Trial x ' num2str(xp(:)')]);
disp 'Point rejected since out of bounds';
end
function print_current_valid_try(max,xp,x,fp,f)
disp(['Current x ' num2str(x(:)')]);
if(max);
disp(['Current f ' num2str(f)]);
disp(['Trial x ' num2str(xp(:)')]);
disp(['Resulting f ' num2str(fp)]);
else
disp(['Current f ' num2str(-f)]);
disp(['Trial x ' num2str(xp(:)')]);
disp(['Resulting f ' num2str(-fp)]);
end;
end
function print_intermediate_statistics(max,t,xopt,vm,fopt,nup,ndown,nrej,lnobds,nnew)
totmov=nup+ndown+nrej;
fprintf('\nIntermediate results before next temperature reduction\n');
disp(['current temperature ' num2str(t)]);
if(max)
disp(['Max function value so far ' num2str(fopt)]);
disp(['Total moves ' num2str(totmov)]);
disp(['Uphill ' num2str(nup)]);
disp(['Accepted downhill ' num2str(ndown)]);
disp(['Rejected downhill ' num2str(nrej)]);
disp(['Out of bounds trials ' num2str(lnobds)]);
disp(['New maxima this temperature ' num2str(nnew)]);
else
disp(['Min function value so far ' num2str(-fopt)]);
disp(['Total moves ' num2str(totmov)]);
disp(['Downhill ' num2str(nup)]);
disp(['Accepted uphill ' num2str(ndown)]);
disp(['Rejected uphill ' num2str(nrej)]);
disp(['Trials out of bounds ' num2str(lnobds)]);
disp(['New minima this temperature ' num2str(nnew)]);
end
xopt1=xopt(1:round(length(xopt)/2));
xopt2=xopt(round(length(xopt)/2)+1:end);
disp(['Current optimal x1 ' num2str(xopt1')]);
disp(['Current optimal x2 ' num2str(xopt2')]);
disp(['Strength(vm) ' num2str(vm')]);
fprintf('\n');
end

991
matlab/optimization/solvopt.m Normal file
View File

@ -0,0 +1,991 @@
function [x,f,exitflag,n_f_evals,n_grad_evals,n_constraint_evals,n_constraint_gradient_evals]=solvopt(x,fun,grad,func,gradc,optim,varargin)
% [x,f,options]=solvopt(x,fun,grad,func,gradc,options,varargin)
%
% The function SOLVOPT, developed by Alexei Kuntsevich and Franz Kappe,
% performs a modified version of Shor's r-algorithm in
% order to find a local minimum resp. maximum of a nonlinear function
% defined on the n-dimensional Euclidean space or % a solution of a nonlinear
% constrained problem:
% min { f(x): g(x) (<)= 0, g(x) in R(m), x in R(n) }
%
% Inputs:
% x n-vector (row or column) of the coordinates of the starting
% point,
% fun name of an M-file (M-function) which computes the value
% of the objective function <fun> at a point x,
% synopsis: f=fun(x)
% grad name of an M-file (M-function) which computes the gradient
% vector of the function <fun> at a point x,
% synopsis: g=grad(x)
% func name of an M-file (M-function) which computes the MAXIMAL
% RESIDUAL(!) for a set of constraints at a point x,
% synopsis: fc=func(x)
% gradc name of an M-file (M-function) which computes the gradient
% vector for the maximal residual constraint at a point x,
% synopsis: gc=gradc(x)
% optim Options structure with fields:
% optim.minimizer_indicator= H, where sign(H)=-1 resp. sign(H)=+1 means minimize
% resp. maximize <fun> (valid only for unconstrained problem)
% and H itself is a factor for the initial trial step size
% (optim.minimizer_indicator=-1 by default),
% optim.TolX= relative error for the argument in terms of the
% infinity-norm (1.e-4 by default),
% optim.TolFun= relative error for the function value (1.e-6 by default),
% optim.MaxIter= limit for the number of iterations (15000 by default),
% optim.verbosity= control of the display of intermediate results and error
% resp. warning messages (default value is 0, i.e., no intermediate
% output but error and warning messages, see more in the manual),
% optim.TolXConstraint= admissible maximal residual for a set of constraints
% (optim.TolXConstraint=1e-8 by default, see more in the manual),
% *optim.SpaceDilation= the coefficient of space dilation (2.5 by default),
% *optim.LBGradientStep= lower bound for the stepsize used for the difference
% approximation of gradients (1e-12 by default, see more in the manual).
% (* ... changes should be done with care)
%
% Outputs:
% x optimal parameter vector (row resp. column),
% f optimum function value
% exitflag: the number of iterations, if positive,
% or an abnormal stop code, if negative (see more in the manual),
% n_f_evals: number of objective evaluations
% n_grad_evals: number of gradient evaluations,
% n_constraint_evals: number of constraint function evaluations,
% n_constraint_gradient_evals number of constraint gradient evaluations.
%
%
% Algorithm: Kuntsevich, A.V., Kappel, F., SolvOpt - The solver for local nonlinear optimization problems
% (version 1.1, Matlab, C, FORTRAN). University of Graz, Graz, 1997.
%
%
% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
% Copyright (C) 2008-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% strings: ----{
errmes='SolvOpt error:';
wrnmes='SolvOpt warning:';
error1='No function name and/or starting point passed to the function.';
error2='Argument X has to be a row or column vector of dimension > 1.';
error30='<fun> returns an empty string.';
error31='Function value does not exist (NaN is returned).';
error32='Function equals infinity at the point.';
error40='<grad> returns an improper matrix. Check the dimension.';
error41='Gradient does not exist (NaN is returned by <grad>).';
error42='Gradient equals infinity at the starting point.';
error43='Gradient equals zero at the starting point.';
error50='<func> returns an empty string.';
error51='<func> returns NaN at the point.';
error52='<func> returns infinite value at the point.';
error60='<gradc> returns an improper vector. Check the dimension';
error61='<gradc> returns NaN at the point.';
error62='<gradc> returns infinite vector at the point.';
error63='<gradc> returns zero vector at an infeasible point.';
error5='Function is unbounded.';
error6='Choose another starting point.';
warn1= 'Gradient is zero at the point, but stopping criteria are not fulfilled.';
warn20='Normal re-setting of a transformation matrix.' ;
warn21='Re-setting due to the use of a new penalty coefficient.' ;
warn4= 'Iterations limit exceeded.';
warn31='The function is flat in certain directions.';
warn32='Trying to recover by shifting insensitive variables.';
warn09='Re-run from recorded point.';
warn08='Ravine with a flat bottom is detected.';
termwarn0='SolvOpt: Normal termination.';
termwarn1='SolvOpt: Termination warning:';
appwarn='The above warning may be reasoned by inaccurate gradient approximation';
endwarn=[...
'Premature stop is possible. Try to re-run the routine from the obtained point. ';...
'Result may not provide the optimum. The function apparently has many extremum points. ';...
'Result may be inaccurate in the coordinates. The function is flat at the optimum. ';...
'Result may be inaccurate in a function value. The function is extremely steep at the optimum.'];
% ----}
% ARGUMENTS PASSED ----{
if nargin<2 % Function and/or starting point are not specified
exitflag=-1;
disp(errmes);
disp(error1);
return
end
if nargin<3
app=1; % No user-supplied gradients
elseif isempty(grad)
app=1;
else
app=0; % Exact gradients are supplied
end
% OPTIONS ----{
doptions.minimizer_indicator=1;
doptions.TolX=1e-6; %accuracy of argument
doptions.TolFun=1e-6; %accuracy of function (see Solvopt p.29)
doptions.MaxIter=15000;
doptions.verbosity=1;
doptions.TolXConstraint=1.e-8;
doptions.SpaceDilation=2.5;
doptions.LBGradientStep=1.e-11;
if nargin<4
optim=doptions;
elseif isempty(optim)
optim=doptions;
end
% Check the values:
optim.TolX=max(optim.TolX,1.e-12);
optim.TolFun=max(optim.TolFun,1.e-12);
optim.TolX=max(optim.LBGradientStep*1.e2,optim.TolX);
optim.TolX=min(optim.TolX,1);
optim.TolFun=min(optim.TolFun,1);
optim.TolXConstraint=max(optim.TolXConstraint,1e-12);
optim.SpaceDilation=max([optim.SpaceDilation,1.5]);
optim.LBGradientStep=max(optim.LBGradientStep,1e-11);
% ----}
if isempty(func)
constr=0;
else
constr=1; % Constrained problem
if isempty(gradc),
appconstr=1;
else
appconstr=0; % Exact gradients of constraints are supplied
end
end
% ----}
% STARTING POINT ----{
if max(size(x))<=1
disp(errmes);
disp(error2);
exitflag=-2;
return
elseif size(x,2)==1
n=size(x,1);
x=x';
trx=1;
elseif size(x,1)==1
n=size(x,2);
trx=0;
else
disp(errmes);
disp(error2);
exitflag=-2;
return
end
% ----}
% WORKING CONSTANTS AND COUNTERS ----{
n_f_evals=0; n_grad_evals=0; % function and gradient calculations
if constr
n_constraint_evals=0;
n_constraint_gradient_evals=0; % same for constraints
end
epsnorm=1.e-15;
epsnorm2=1.e-30; % epsilon & epsilon^2
if constr, h1=-1; % NLP: restricted to minimization
cnteps=optim.TolXConstraint; % Max. admissible residual
else
h1=sign(optim.minimizer_indicator); % Minimize resp. maximize a function
end
k=0; % Iteration counter
wdef=1/optim.SpaceDilation-1; % Default space transf. coeff.
%Gamma control ---{
ajb=1+.1/n^2; % Base I
ajp=20;
ajpp=ajp; % Start value for the power
ajs=1.15; % Base II
knorms=0; gnorms=zeros(1,10); % Gradient norms stored
%---}
%Display control ---{
if optim.verbosity<=0, dispdata=0;
if optim.verbosity==-1
dispwarn=0;
else
dispwarn=1;
end
else
dispdata=round(optim.verbosity);
dispwarn=1;
end
ld=dispdata;
%---}
%Stepsize control ---{
dq=5.1; % Step divider (at f_{i+1}>gamma*f_{i})
du20=2;du10=1.5;du03=1.05; % Step multipliers (at certain steps made)
kstore=3;nsteps=zeros(1,kstore); % Steps made at the last 'kstore' iterations
if app
des=6.3; % Desired number of steps per 1-D search
else
des=3.3;
end
mxtc=3; % Number of trial cycles (steep wall detect)
%---}
termx=0; limxterm=50; % Counter and limit for x-criterion
ddx =max(1e-11,optim.LBGradientStep); % stepsize for gradient approximation
low_bound=-1+1e-4; % Lower bound cosine used to detect a ravine
ZeroGrad=n*1.e-16; % Lower bound for a gradient norm
nzero=0; % Zero-gradient events counter
% Lower bound for values of variables taking into account
lowxbound=max([optim.TolX,1e-3]);
% Lower bound for function values to be considered as making difference
lowfbound=optim.TolFun^2;
krerun=0; % Re-run events counter
detfr=optim.TolFun*100; % relative error for f/f_{record}
detxr=optim.TolX*10; % relative error for norm(x)/norm(x_{record})
warnno=0; % the number of warn.mess. to end with
kflat=0; % counter for points of flatness
stepvanish=0; % counter for vanished steps
stopf=0;
% ----} End of setting constants
% ----} End of the preamble
% COMPUTE THE FUNCTION ( FIRST TIME ) ----{
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if isempty(f), if dispwarn,disp(errmes);disp(error30);end
exitflag=-3; if trx, x=x';end, return
elseif isnan(f), if dispwarn,disp(errmes);disp(error31);disp(error6);end
exitflag=-3; if trx, x=x';end, return
elseif abs(f)==Inf, if dispwarn,disp(errmes);disp(error32);disp(error6);end
exitflag=-3; if trx, x=x';end, return
end
xrec=x; frec=f; % record point and function value
% Constrained problem
if constr, fp=f; kless=0;
if trx,
fc=feval(func,x');
else
fc=feval(func,x);
end
if isempty(fc),
if dispwarn,disp(errmes);disp(error50);end
exitflag=-5; if trx, x=x';end, return
elseif isnan(fc),
if dispwarn,disp(errmes);disp(error51);disp(error6);end
exitflag=-5; if trx, x=x';end, return
elseif abs(fc)==Inf,
if dispwarn,disp(errmes);disp(error52);disp(error6);end
exitflag=-5; if trx, x=x';end, return
end
n_constraint_evals=n_constraint_evals+1;
PenCoef=1; % first rough approximation
if fc<=cnteps
FP=1; fc=0; % feasible point
else
FP=0; % infeasible point
end
f=f+PenCoef*fc;
end
% ----}
% COMPUTE THE GRADIENT ( FIRST TIME ) ----{
if app
deltax=h1*ddx*ones(size(x));
if constr
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,f,fun,deltax,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1, g=g'; end, ng=norm(g);
if size(g,2)~=n, if dispwarn,disp(errmes);disp(error40);end
exitflag=-4; if trx, x=x';end, return
elseif isnan(ng), if dispwarn,disp(errmes);disp(error41);disp(error6);end
exitflag=-4; if trx, x=x';end, return
elseif ng==Inf, if dispwarn,disp(errmes);disp(error42);disp(error6);end
exitflag=-4; if trx, x=x';end, return
elseif ng<ZeroGrad, if dispwarn,disp(errmes);disp(error43);disp(error6);end
exitflag=-4; if trx, x=x';end, return
end
if constr
if ~FP
if appconstr,
deltax=sign(x); idx=find(deltax==0);
deltax(idx)=ones(size(idx));
deltax=ddx*deltax;
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
gc=feval(gradc,x);
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
if size(gc,2)==1
gc=gc';
end
ngc=norm(gc);
if size(gc,2)~=n,
if dispwarn
disp(errmes);
disp(error60);
end
exitflag=-6;
if trx
x=x';
end
return
elseif isnan(ngc),
if dispwarn
disp(errmes);
disp(error61);
disp(error6);
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc==Inf,
if dispwarn
disp(errmes);
disp(error62);
disp(error6);
end
exitflag=-6;
if trx
x=x';
end
return
elseif ngc<ZeroGrad,
if dispwarn
disp(errmes);
disp(error63);
end
exitflag=-6;
if trx
x=x';
end
return
end
g=g+PenCoef*gc; ng=norm(g);
end, end
grec=g; nng=ng;
% ----}
% INITIAL STEPSIZE
h=h1*sqrt(optim.TolX)*max(abs(x)); % smallest possible stepsize
if abs(optim.minimizer_indicator)~=1,
h=h1*max(abs([optim.minimizer_indicator,h])); % user-supplied stepsize
else
h=h1*max(1/log(ng+1.1),abs(h)); % calculated stepsize
end
% RESETTING LOOP ----{
while 1,
kcheck=0; % Set checkpoint counter.
kg=0; % stepsizes stored
kj=0; % ravine jump counter
B=eye(n); % re-set transf. matrix to identity
fst=f; g1=g; dx=0;
% ----}
% MAIN ITERATIONS ----{
while 1,
k=k+1;kcheck=kcheck+1;
laststep=dx;
% ADJUST GAMMA --{
gamma=1+max([ajb^((ajp-kcheck)*n),2*optim.TolFun]);
gamma=min([gamma,ajs^max([1,log10(nng+1)])]);
% --}
gt=g*B; w=wdef;
% JUMPING OVER A RAVINE ----{
if (gt/norm(gt))*(g1'/norm(g1))<low_bound
if kj==2, xx=x; end, if kj==0, kd=4; end,
kj=kj+1; w=-.9; h=h*2; % use large coef. of space dilation
if kj>2*kd, kd=kd+1; warnno=1;
if any(abs(x-xx)<epsnorm*abs(x)), % flat bottom is detected
if dispwarn,disp(wrnmes);disp(warn08); end
end
end
else
kj=0;
end
% ----}
% DILATION ----{
z=gt-g1;
nrmz=norm(z);
if(nrmz>epsnorm*norm(gt))
z=z/nrmz;
g1=gt+w*(z*gt')*z; B=B+w*(B*z')*z;
else
z=zeros(1,n);
nrmz=0;
g1=gt;
end
d1=norm(g1); g0=(g1/d1)*B';
% ----}
% RESETTING ----{
if kcheck>1
idx=find(abs(g)>ZeroGrad); numelem=size(idx,2);
if numelem>0, grbnd=epsnorm*numelem^2;
if all(abs(g1(idx))<=abs(g(idx))*grbnd) | nrmz==0
if dispwarn, disp(wrnmes); disp(warn20); end
if abs(fst-f)<abs(f)*.01
ajp=ajp-10*n;
else
ajp=ajpp;
end
h=h1*dx/3;
k=k-1;
break
end
end
end
% ----}
% STORE THE CURRENT VALUES AND SET THE COUNTERS FOR 1-D SEARCH
xopt=x;fopt=f; k1=0;k2=0;ksm=0;kc=0;knan=0; hp=h;
if constr, Reset=0; end
% 1-D SEARCH ----{
while 1,
x1=x;f1=f;
if constr, FP1=FP; fp1=fp; end
x=x+hp*g0;
% FUNCTION VALUE
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if h1*f==Inf
if dispwarn
disp(errmes);
disp(error5);
end
exitflag=-7;
if trx
x=x';
end
return
end
if constr, fp=f;
if trx
fc=feval(func,x');
else
fc=feval(func,x);
end
n_constraint_evals=n_constraint_evals+1;
if isnan(fc),
if dispwarn,disp(errmes);disp(error51);disp(error6);end
exitflag=-5; if trx, x=x';end, return
elseif abs(fc)==Inf,
if dispwarn,disp(errmes);disp(error52);disp(error6);end
exitflag=-5; if trx, x=x';end, return
end
if fc<=cnteps
FP=1;
fc=0;
else
FP=0;
fp_rate=(fp-fp1);
if fp_rate<-epsnorm
if ~FP1
PenCoefNew=-15*fp_rate/norm(x-x1);
if PenCoefNew>1.2*PenCoef,
PenCoef=PenCoefNew; Reset=1; kless=0; f=f+PenCoef*fc; break
end
end
end
end
f=f+PenCoef*fc;
end
if abs(f)==Inf || isnan(f)
if dispwarn, disp(wrnmes);
if isnan(f)
disp(error31);
else
disp(error32);
end
end
if ksm || kc>=mxtc
exitflag=-3;
if trx
x=x';
end
return
else
k2=k2+1;
k1=0;
hp=hp/dq;
x=x1;
f=f1;
knan=1;
if constr
FP=FP1;
fp=fp1;
end
end
% STEP SIZE IS ZERO TO THE EXTENT OF EPSNORM
elseif all(abs(x-x1)<abs(x)*epsnorm),
stepvanish=stepvanish+1;
if stepvanish>=5,
exitflag=-14;
if dispwarn, disp(termwarn1);
disp(endwarn(4,:)); end
if trx,x=x';end, return
else
x=x1;
f=f1;
hp=hp*10;
ksm=1;
if constr
FP=FP1;
fp=fp1;
end
end
% USE SMALLER STEP
elseif h1*f<h1*gamma^sign(f1)*f1
if ksm,break,end, k2=k2+1;k1=0; hp=hp/dq; x=x1;f=f1;
if constr, FP=FP1; fp=fp1; end
if kc>=mxtc, break, end
% 1-D OPTIMIZER IS LEFT BEHIND
else if h1*f<=h1*f1, break, end
% USE LARGER STEP
k1=k1+1; if k2>0, kc=kc+1; end, k2=0;
if k1>=20, hp=du20*hp;
elseif k1>=10, hp=du10*hp;
elseif k1>=3, hp=du03*hp;
end
end
end
% ----} End of 1-D search
% ADJUST THE TRIAL STEP SIZE ----{
dx=norm(xopt-x);
if kg<kstore, kg=kg+1; end
if kg>=2, nsteps(2:kg)=nsteps(1:kg-1); end
nsteps(1)=dx/(abs(h)*norm(g0));
kk=sum(nsteps(1:kg).*[kg:-1:1])/sum([kg:-1:1]);
if kk>des
if kg==1
h=h*(kk-des+1);
else
h=h*sqrt(kk-des+1);
end
elseif kk<des
h=h*sqrt(kk/des);
end
stepvanish=stepvanish+ksm;
% ----}
% COMPUTE THE GRADIENT ----{
if app,
deltax=sign(g0); idx=find(deltax==0);
deltax(idx)=ones(size(idx)); deltax=h1*ddx*deltax;
if constr
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x ,f,fun,deltax ,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1, g=g'; end, ng=norm(g);
if isnan(ng),
if dispwarn, disp(errmes); disp(error41); end
exitflag=-4; if trx, x=x'; end, return
elseif ng==Inf,
if dispwarn,disp(errmes);disp(error42);end
exitflag=-4; if trx, x=x';end, return
elseif ng<ZeroGrad,
if dispwarn,disp(wrnmes);disp(warn1);end
ng=ZeroGrad;
end
% Constraints:
if constr
if ~FP
if ng<.01*PenCoef
kless=kless+1;
if kless>=20, PenCoef=PenCoef/10; Reset=1; kless=0; end
else
kless=0;
end
if appconstr,
deltax=sign(x); idx=find(deltax==0);
deltax(idx)=ones(size(idx)); deltax=ddx*deltax;
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
gc=feval(gradc,x );
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
if size(gc,2)==1, gc=gc'; end, ngc=norm(gc);
if isnan(ngc),
if dispwarn,disp(errmes);disp(error61);end
exitflag=-6; if trx, x=x';end, return
elseif ngc==Inf,
if dispwarn,disp(errmes);disp(error62);end
exitflag=-6; if trx, x=x';end, return
elseif ngc<ZeroGrad && ~appconstr,
if dispwarn,disp(errmes);disp(error63);end
exitflag=-6; if trx, x=x';end, return
end
g=g+PenCoef*gc; ng=norm(g);
if Reset, if dispwarn, disp(wrnmes); disp(warn21); end
h=h1*dx/3; k=k-1; nng=ng; break
end
end, end
if h1*f>h1*frec, frec=f; xrec=x; grec=g; end
% ----}
if ng>ZeroGrad,
if knorms<10, knorms=knorms+1; end
if knorms>=2, gnorms(2:knorms)=gnorms(1:knorms-1); end
gnorms(1)=ng;
nng=(prod(gnorms(1:knorms)))^(1/knorms);
end
% DISPLAY THE CURRENT VALUES ----{
if k==ld
disp('Iter.# ..... Function ... Step Value ... Gradient Norm ');
disp(sprintf('%5i %13.5e %13.5e %13.5e',k,f,dx,ng));
ld=k+dispdata;
end
%----}
% CHECK THE STOPPING CRITERIA ----{
termflag=1;
if constr, if ~FP, termflag=0; end, end
if kcheck<=5, termflag=0; end
if knan, termflag=0; end
if kc>=mxtc, termflag=0; end
% ARGUMENT
if termflag
idx=find(abs(x)>=lowxbound);
if isempty(idx) || all(abs(xopt(idx)-x(idx))<=optim.TolX*abs(x(idx)))
termx=termx+1;
% FUNCTION
if abs(f-frec)> detfr * abs(f) && ...
abs(f-fopt)<=optim.TolFun*abs(f) && ...
krerun<=3 && ...
~constr
if any(abs(xrec(idx)-x(idx))> detxr * abs(x(idx)))
if dispwarn
disp(wrnmes);
disp(warn09);
end
x=xrec;
f=frec;
g=grec;
ng=norm(g);
krerun=krerun+1;
h=h1*max([dx,detxr*norm(x)])/krerun;
warnno=2;
break
else
h=h*10;
end
elseif abs(f-frec)> optim.TolFun*abs(f) && ...
norm(x-xrec)<optim.TolX*norm(x) && constr
elseif abs(f-fopt)<=optim.TolFun*abs(f) || ...
abs(f)<=lowfbound || ...
(abs(f-fopt)<=optim.TolFun && termx>=limxterm )
if stopf
if dx<=laststep
if warnno==1 && ng<sqrt(optim.TolFun), warnno=0; end
if ~app, if any(abs(g)<=epsnorm2), warnno=3; end, end
if warnno~=0
exitflag=-warnno-10;
if dispwarn, disp(termwarn1);
disp(endwarn(warnno,:));
if app, disp(appwarn); end
end
else
exitflag=k;
if dispwarn
disp(termwarn0);
end
end
if trx
x=x';
end
return
end
else
stopf=1;
end
elseif dx<1.e-12*max(norm(x),1) && termx>=limxterm
exitflag=-14;
if dispwarn, disp(termwarn1); disp(endwarn(4,:));
if app, disp(appwarn); end
end
x=xrec; f=frec;
if trx,x=x';end, return
else
stopf=0;
end
end
end
% ITERATIONS LIMIT
if(k==optim.MaxIter)
exitflag=-9;
if trx
x=x';
end,
if dispwarn, disp(wrnmes); disp(warn4); end
return
end
% ----}
% ZERO GRADIENT ----{
if constr
if ng<=ZeroGrad,
if dispwarn, disp(termwarn1); disp(warn1); end
exitflag=-8;
if trx
x=x';
end
return
end
else
if ng<=ZeroGrad, nzero=nzero+1;
if dispwarn, disp(wrnmes); disp(warn1); end
if nzero>=3
exitflag=-8;
if trx
x=x';
end
return
end
g0=-h*g0/2;
for i=1:10,
x=x+g0;
if trx
f=feval(fun,x',varargin{:});
else
f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f)==Inf
if dispwarn
disp(errmes);
disp(error32);
end
exitflag=-3;
if trx
x=x';
end
return
elseif isnan(f),
if dispwarn
disp(errmes);
disp(error32);
end
exitflag=-3;
if trx
x=x';
end
return
end
if app,
deltax=sign(g0);
idx=find(deltax==0);
deltax(idx)=ones(size(idx));
deltax=h1*ddx*deltax;
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x,f,fun,deltax,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
if trx
g=feval(grad,x',varargin{:});
else
g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1
g=g';
end
ng=norm(g);
if ng==Inf
if dispwarn
disp(errmes);
disp(error42);
end
exitflag=-4;
if trx
x=x';
end
return
elseif isnan(ng)
if dispwarn
disp(errmes);
disp(error41);
end
exitflag=-4;
if trx
x=x';
end
return
end
if ng>ZeroGrad
break
end
end
if ng<=ZeroGrad,
if dispwarn
disp(termwarn1);
disp(warn1);
end
exitflag=-8;
if trx
x=x';
end
return
end
h=h1*dx;
break
end
end
% ----}
% FUNCTION IS FLAT AT THE POINT ----{
if ~constr && abs(f-fopt)<abs(fopt)*optim.TolFun && kcheck>5 && ng<1
idx=find(abs(g)<=epsnorm2);
ni=size(idx,2);
if ni>=1 && ni<=n/2 && kflat<=3, kflat=kflat+1;
if dispwarn, disp(wrnmes); disp(warn31); end, warnno=1;
x1=x; fm=f;
for j=idx, y=x(j); f2=fm;
if y==0, x1(j)=1; elseif abs(y)<1, x1(j)=sign(y); else, x1(j)=y; end
for i=1:20, x1(j)=x1(j)/1.15;
if trx
f1=feval(fun,x1',varargin{:});
else
f1=feval(fun,x1,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f1)~=Inf && ~isnan(f1),
if h1*f1>h1*fm
y=x1(j);
fm=f1;
elseif h1*f2>h1*f1
break
elseif f2==f1
x1(j)=x1(j)/1.5;
end
f2=f1;
end
end
x1(j)=y;
end
if h1*fm>h1*f
if app,
deltax=h1*ddx*ones(size(deltax));
if trx
gt=apprgrdn(x1',fm,fun,deltax',1,varargin{:});
else
gt=apprgrdn(x1 ,fm,fun,deltax ,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
if trx
gt=feval(grad,x1',varargin{:});
else
gt=feval(grad,x1,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(gt,2)==1
gt=gt';
end
ngt=norm(gt);
if ~isnan(ngt) && ngt>epsnorm2,
if dispwarn
disp(warn32);
end
optim.TolFun=optim.TolFun/5;
x=x1;
g=gt;
ng=ngt;
f=fm;
h=h1*dx/3;
break
end
end
end
end
% ----}
end % iterations
end % restart
% end of the function
%
end

2
matlab/optimize_prior.m
View File

@ -51,7 +51,7 @@ objective_function_penalty_base = minus_logged_prior_density(xinit, BayesInfo.ps
BayesInfo.p4,DynareOptions,ModelInfo,EstimationInfo,DynareResults);
% Maximization of the prior density
[xparams, lpd, hessian] = ...
[xparams, lpd, hessian_mat] = ...
maximize_prior_density(xinit, BayesInfo.pshape, ...
BayesInfo.p6, ...
BayesInfo.p7, ...

49
matlab/parallel/masterParallel.m
View File

@ -3,28 +3,38 @@ function [fOutVar,nBlockPerCPU, totCPU] = masterParallel(Parallel,fBlock,nBlock,
% This is the most important function for the management of DYNARE parallel
% computing.
% It is the top-level function called on the master computer when parallelizing a task.
% This function has two main computational strategies for managing the matlab worker (slave process).
%
% This function has two main computational strategies for managing the
% matlab worker (slave process):
%
% 0 Simple Close/Open Stategy:
% In this case the new Matlab instances (slave process) are open when
% necessary and then closed. This can happen many times during the
% simulation of a model.
% In this case the new Matlab instances (slave process) are open when
% necessary and then closed. This can happen many times during the
% simulation of a model.
%
% 1 Always Open Strategy:
% In this case we have a more sophisticated management of slave processes,
% which are no longer closed at the end of each job. The slave processes
% wait for a new job (if it exists). If a slave does not receive a new job after a
% fixed time it is destroyed. This solution removes the computational
% time necessary to Open/Close new Matlab instances.
% In this case we have a more sophisticated management of slave processes,
% which are no longer closed at the end of each job. The slave processes
% wait for a new job (if it exists). If a slave does not receive a new job after a
% fixed time it is destroyed. This solution removes the computational
% time necessary to Open/Close new Matlab instances.
%
% The first (point 0) is the default Strategy
% i.e.(Parallel_info.leaveSlaveOpen=0). This value can be changed by the
% user in xxx.mod file or it is changed by the programmer if it is necessary to
% reduce the overall computational time. See for example the
% prior_posterior_statistics.m.
%
% The number of parallelized threads will be equal to (nBlock-fBlock+1).
%
% Treatment of global variables:
% Global variables used within the called function (e.g.
% objective_function_penalty_base) are wrapped and passed by storing their
% values at the start of the parallel computation in a file via
% storeGlobalVars.m. This file is then loaded in the separate,
% independent slave Matlab sessions. By keeping them separate, no
% interaction via global variables can take place.
%
% INPUTS
% o Parallel [struct vector] copy of options_.parallel
% o fBlock [int] index number of the first thread
@ -53,7 +63,7 @@ function [fOutVar,nBlockPerCPU, totCPU] = masterParallel(Parallel,fBlock,nBlock,
% the number of CPUs declared in "Parallel", if
% the number of required threads is lower)
% Copyright (C) 2009-2013 Dynare Team
% Copyright (C) 2009-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -126,7 +136,7 @@ if isHybridMatlabOctave || isoctave
end
end
if exist('fGlobalVar') && ~isempty(fGlobalVar),
if exist('fGlobalVar','var') && ~isempty(fGlobalVar),
fInputNames = fieldnames(fGlobalVar);
for j=1:length(fInputNames),
TargetVar = fGlobalVar.(fInputNames{j});
@ -161,7 +171,7 @@ switch Strategy
save([fname,'_input.mat'],'fInputVar','Parallel','-append')
case 1
if exist('fGlobalVar'),
if exist('fGlobalVar','var'),
save(['temp_input.mat'],'fInputVar','fGlobalVar')
else
save(['temp_input.mat'],'fInputVar')
@ -540,7 +550,6 @@ else
'Renderer','Painters', ...
'Resize','off');
vspace = 0.1;
ncol = ceil(totCPU/10);
hspace = 0.9/ncol;
hstatus(1) = axes('position',[0.05/ncol 0.92 0.9/ncol 0.03], ...
@ -882,8 +891,4 @@ switch Strategy
end
end
end
end
end

12
matlab/perfect-foresight-models/perfect_foresight_setup.m
View File

@ -34,8 +34,16 @@ global M_ options_ oo_
test_for_deep_parameters_calibration(M_);
if size(M_.lead_lag_incidence,2)-nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)) > 0
mess = ['PERFECT_FORESIGHT_SETUP: error in model specification : variable ' M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:)];
mess = [mess ' doesn''t appear as current variable.'];
mess = ['PERFECT_FORESIGHT_SETUP: error in model specification : the variable(s) '];
var_list=M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:);
for i=1:size(var_list,1)
if i<size(var_list,1)
mess = [mess, deblank(var_list(i,:)) ', '];
else
mess = [mess, deblank(var_list(i,:)) ];
end
end
mess = [mess ' don''t appear as current period variables.'];
error(mess)
end

4
matlab/perfect-foresight-models/perfect_foresight_solver.m
View File

@ -65,9 +65,11 @@ oo_ = simulation_core(options_, M_, oo_);
if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
skipline()
disp('Simulation of the perfect foresight model failed!')
disp('Switching to a homotopy method...')
skipline()
% Disable warnings if homotopy
warning_old_state = warning;
warning off all
% Do not print anything
oldverbositylevel = options_.verbosity;
@ -147,7 +149,7 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
fprintf('++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n')
skipline()
options_.verbosity = oldverbositylevel;
warning on all
warning(warning_old_state);
end
if oo_.deterministic_simulation.status == 1

11
matlab/perfect-foresight-models/private/check_input_arguments.m
View File

@ -1,5 +1,7 @@
function check_input_arguments(DynareOptions, DynareModel, DynareResults)
%function check_input_arguments(DynareOptions, DynareModel, DynareResults)
%Conducts checks for inconsistent/missing inputs to deterministic
%simulations
% Copyright (C) 2015 Dynare Team
%
@ -42,8 +44,8 @@ end
if isempty(DynareResults.endo_simul) || any(size(DynareResults.endo_simul) ~= [ DynareModel.endo_nbr, DynareModel.maximum_lag+DynareOptions.periods+DynareModel.maximum_lead ])
if options_.initval_file
disp(sprintf('PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Check whether your initval-file provides %d periods.',M_.maximum_endo_lag+options_.periods+M_.maximum_endo_lead))
if DynareOptions.initval_file
fprintf('PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Check whether your initval-file provides %d periods.',DynareModel.maximum_endo_lag+DynareOptions.periods+DynareModel.maximum_endo_lead)
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Did you run ''perfect_foresight_setup'' ?')
else
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Did you run ''perfect_foresight_setup'' ?')
@ -52,11 +54,10 @@ end
if (DynareModel.exo_nbr > 0) && ...
(isempty(DynareResults.exo_simul) || any(size(DynareResults.exo_simul) ~= [ DynareModel.maximum_lag+DynareOptions.periods+DynareModel.maximum_lead, DynareModel.exo_nbr ]))
if options_.initval_file
disp(sprintf('PERFECT_FORESIGHT_SOLVER: ''oo_.exo_simul'' has wrong size. Check whether your initval-file provides %d periods.',M_.maximum_endo_lag+options_.periods+M_.maximum_endo_lead))
if DynareOptions.initval_file
fprintf('PERFECT_FORESIGHT_SOLVER: ''oo_.exo_simul'' has wrong size. Check whether your initval-file provides %d periods.',DynareModel.maximum_endo_lag+DynareOptions.periods+DynareModel.maximum_endo_lead)
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.exo_simul'' has wrong size.')
else
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.exo_simul'' has wrong size. Did you run ''perfect_foresight_setup'' ?')
end
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.exo_simul'' has wrong size. Did you run ''perfect_foresight_setup'' ?')
end

8
matlab/perfect-foresight-models/private/simulation_core.m
View File

@ -75,9 +75,9 @@ else
yT = y(:,periods+2);
z = y(:,2:periods+1);
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
[i_cols,junk,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
[i_cols_J1,junk,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
[y,info] = dynare_solve(@perfect_foresight_problem,z(:),1, ...
str2func([M_.fname '_dynamic']),y0,yT, ...
@ -109,9 +109,9 @@ if nargout>1
yy = oo_.endo_simul(:,2:options_.periods+1);
if ~exist('illi')
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
[i_cols,junk,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
[i_cols_J1,junk,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
end
if options_.block && ~options_.bytecode

30
matlab/perfect-foresight-models/sim1.m
View File

@ -69,7 +69,7 @@ i_cols_A1 = find(lead_lag_incidence(2:3,:)');
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
i_cols_0 = nonzeros(lead_lag_incidence(2,:)');
i_cols_A0 = find(lead_lag_incidence(2,:)');
i_cols_j = 1:nd;
i_cols_j = (1:nd)';
i_upd = maximum_lag*ny+(1:periods*ny);
Y = endo_simul(:);
@ -86,7 +86,6 @@ z = Y(find(lead_lag_incidence'));
[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
steady_state,maximum_lag+1);
A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
res = zeros(periods*ny,1);
o_periods = periods;
@ -94,26 +93,32 @@ o_periods = periods;
ZERO = zeros(length(i_upd),1);
h1 = clock ;
iA = zeros(periods*M_.NNZDerivatives(1),3);
for iter = 1:options_.simul.maxit
h2 = clock ;
i_rows = 1:ny;
i_rows = (1:ny)';
i_cols_A = find(lead_lag_incidence');
i_cols = i_cols_A+(maximum_lag-1)*ny;
m = 0;
for it = (maximum_lag+1):(maximum_lag+periods)
[d1,jacobian] = model_dynamic(Y(i_cols), exo_simul, params, steady_state,it);
[d1,jacobian] = model_dynamic(Y(i_cols), exo_simul, params, ...
steady_state,it);
if it == maximum_lag+periods && it == maximum_lag+1
A(i_rows,i_cols_A0) = jacobian(:,i_cols_0);
[r,c,v] = find(jacobian(:,i_cols_0));
iA((1:length(v))+m,:) = [i_rows(r),i_cols_A0(c),v];
elseif it == maximum_lag+periods
A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T);
[r,c,v] = find(jacobian(:,i_cols_T));
iA((1:length(v))+m,:) = [i_rows(r),i_cols_A(i_cols_T(c)),v];
elseif it == maximum_lag+1
A(i_rows,i_cols_A1) = jacobian(:,i_cols_1);
[r,c,v] = find(jacobian(:,i_cols_1));
iA((1:length(v))+m,:) = [i_rows(r),i_cols_A1(c),v];
else
A(i_rows,i_cols_A) = jacobian(:,i_cols_j);
[r,c,v] = find(jacobian(:,i_cols_j));
iA((1:length(v))+m,:) = [i_rows(r),i_cols_A(c),v];
end
m = m + length(v);
res(i_rows) = d1;
if endogenous_terminal_period && iter>1
@ -157,6 +162,9 @@ for iter = 1:options_.simul.maxit
break
end
iA = iA(1:m,:);
A = sparse(iA(:,1),iA(:,2),iA(:,3),periods*ny,periods*ny);
if endogenous_terminal_period && iter>1
dy = ZERO;
dy(1:i_rows(end)) = -A(1:i_rows(end),1:i_rows(end))\res(1:i_rows(end));

23
matlab/plot_identification.m
View File

@ -247,13 +247,30 @@ else
hist(log10(idelre.cond))
title('log10 of Condition number in the LRE model')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_COND' ],options_);
options_mcf.pvalue_ks = 0.1;
options_mcf.pvalue_corr = 0.001;
options_mcf.alpha2 = 0;
options_mcf.param_names = name;
options_mcf.fname_ = M_.fname;
options_mcf.OutputDirectoryName = IdentifDirectoryName;
options_mcf.beha_title = 'LOW condition nbr';
options_mcf.nobeha_title = 'HIGH condition nbr';
options_mcf.amcf_name = 'MC_HighestCondNumberLRE';
options_mcf.amcf_title = 'MC Highest Condition Number LRE Model';
options_mcf.title = 'MC Highest Condition Number LRE Model';
ncut=floor(SampleSize/10*9);
[dum,is]=sort(idelre.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberLRE', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number LRE Model');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
options_mcf.amcf_name = 'MC_HighestCondNumberModel';
options_mcf.amcf_title = 'MC Highest Condition Number Model Solution';
options_mcf.title = 'MC Highest Condition Number Model Solution';
[dum,is]=sort(idemodel.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberModel', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number Model Solution');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
options_mcf.amcf_name = 'MC_HighestCondNumberMoments';
options_mcf.amcf_title = 'MC Highest Condition Number Model Moments';
options_mcf.title = 'MC Highest Condition Number Model Moments';
[dum,is]=sort(idemoments.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberMoments', 1, [], IdentifDirectoryName, 0.1,'MC Highest Condition Number Model Moments');
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
% for j=1:nparam,
% % ibeh=find(idemoments.Mco(j,:)<0.9);

8
matlab/prior_posterior_statistics.m
View File

@ -14,11 +14,11 @@ function prior_posterior_statistics(type,dataset,dataset_info)
%
% SPECIAL REQUIREMENTS
% none
%
% PARALLEL CONTEXT
% See the comments random_walk_metropolis_hastings.m funtion.
% See the comments in the random_walk_metropolis_hastings.m funtion.
%
%
% Copyright (C) 2005-2013 Dynare Team
%
% This file is part of Dynare.

2
matlab/prior_posterior_statistics_core.m
View File

@ -273,6 +273,8 @@ for b=fpar:B
if irun(5) > MAX_nruns || b == B
stock = stock_param(1:irun(5)-1,:);
stock_logpo = stock_logpo(1:irun(5)-1);
stock_ys = stock_ys(1:irun(5)-1,:);
ifil(5) = ifil(5) + 1;
save([DirectoryName '/' M_.fname '_param' int2str(ifil(5)) '.mat'],'stock','stock_logpo','stock_ys');
if RemoteFlag==1,

45
matlab/random_walk_metropolis_hastings.m
View File

@ -1,14 +1,17 @@
function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
%function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
% Random walk Metropolis-Hastings algorithm.
% function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Random Walk Metropolis-Hastings algorithm.
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o ProposalFun [char] string specifying the name of the proposal
% density
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
@ -16,27 +19,27 @@ function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bou
% o oo_ outputs structure
%
% ALGORITHM
% Metropolis-Hastings.
% Random-Walk Metropolis-Hastings.
%
% SPECIAL REQUIREMENTS
% None.
%
% PARALLEL CONTEXT
% The most computationally intensive part of this function may be executed
% in parallel. The code sutable to be executed in
% parallel on multi core or cluster machine (in general a 'for' cycle),
% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
% Then the DYNARE parallel package contain a set of pairs matlab functions that can be executed in
% parallel and called name_function.m and name_function_core.m.
% In addition in parallel package we have second set of functions used
% to manage the parallel computation.
% in parallel. The code suitable to be executed in
% parallel on multi core or cluster machine (in general a 'for' cycle)
% has been removed from this function and been placed in the random_walk_metropolis_hastings_core.m funtion.
%
% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
% to manage the parallel computations.
%
% This function was the first function to be parallelized, later other
% This function was the first function to be parallelized. Later, other
% functions have been parallelized using the same methodology.
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management funtions.
% parallel functions and also for management functions.
% Copyright (C) 2006-2013 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -54,7 +57,7 @@ function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bou
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% In Metropolis, we set penalty to Inf to as to reject all parameter sets triggering error in target density computation
% In Metropolis, we set penalty to Inf so as to reject all parameter sets triggering an error during target density computation
global objective_function_penalty_base
objective_function_penalty_base = Inf;
@ -73,16 +76,16 @@ load_last_mh_history_file(MetropolisFolder, ModelName);
% First run in serial mode, and then comment the follow line.
% save('recordSerial.mat','-struct', 'record');
% For parllel runs after serial runs with the abobe line active.
% For parallel runs after serial runs with the abobe line active.
% TempRecord=load('recordSerial.mat');
% record.Seeds=TempRecord.Seeds;
% Snapshot of the current state of computing. It necessary for the parallel
% execution (i.e. to execute in a corretct way portion of code remotely or
% on many core). The mandatory variables for local/remote parallel
% computing are stored in localVars struct.
% execution (i.e. to execute in a corretct way a portion of code remotely or
% on many cores). The mandatory variables for local/remote parallel
% computing are stored in the localVars struct.
localVars = struct('TargetFun', TargetFun, ...
'ProposalFun', ProposalFun, ...
@ -110,9 +113,8 @@ localVars = struct('TargetFun', TargetFun, ...
'varargin',[]);
% The user don't want to use parallel computing, or want to compute a
% single chain. In this cases Random walk Metropolis-Hastings algorithm is
% computed sequentially.
% User doesn't want to use parallel computing, or wants to compute a
% single chain compute Random walk Metropolis-Hastings algorithm sequentially.
if isnumeric(options_.parallel) || (nblck-fblck)==0,
fout = random_walk_metropolis_hastings_core(localVars, fblck, nblck, 0);
@ -152,6 +154,7 @@ NewFile = fout(1).NewFile;
update_last_mh_history_file(MetropolisFolder, ModelName, record);
% Provide diagnostic output
skipline()
disp(['Estimation::mcmc: Number of mh files: ' int2str(NewFile(1)) ' per block.'])
disp(['Estimation::mcmc: Total number of generated files: ' int2str(NewFile(1)*nblck) '.'])

165
matlab/random_walk_metropolis_hastings_core.m
View File

@ -1,25 +1,25 @@
function myoutput = random_walk_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% PARALLEL CONTEXT
% This function contain the most computationally intensive portion of code in
% random_walk_metropolis_hastings (the 'for xxx = fblck:nblck' loop). The branches in 'for'
% cycle and are completely independent than suitable to be executed in parallel way.
% function myoutput = random_walk_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% Contains the most computationally intensive portion of code in
% random_walk_metropolis_hastings (the 'for xxx = fblck:nblck' loop). The branches in that 'for'
% cycle are completely independent to be suitable for parallel execution.
%
% INPUTS
% o myimput [struc] The mandatory variables for local/remote
% parallel computing obtained from random_walk_metropolis_hastings.m
% function.
% o fblck and nblck [integer] The Metropolis-Hastings chains.
% o whoiam [integer] In concurrent programming a modality to refer to the differents thread running in parallel is needed.
% o whoiam [integer] In concurrent programming a modality to refer to the different threads running in parallel is needed.
% The integer whoaim is the integer that
% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
% cluster.
% o ThisMatlab [integer] Allows us to distinguish between the
% 'main' matlab, the slave matlab worker, local matlab, remote matlab,
% 'main' Matlab, the slave Matlab worker, local Matlab, remote Matlab,
% ... Then it is the index number of this slave machine in the cluster.
% OUTPUTS
% o myoutput [struc]
% If executed without parallel is the original output of 'for b =
% fblck:nblck' otherwise a portion of it computed on a specific core or
% If executed without parallel, this is the original output of 'for b =
% fblck:nblck'. Otherwise, it's a portion of it computed on a specific core or
% remote machine. In this case:
% record;
% irun;
@ -31,23 +31,12 @@ function myoutput = random_walk_metropolis_hastings_core(myinputs,fblck,nblck,wh
%
% SPECIAL REQUIREMENTS.
% None.
%
% PARALLEL CONTEXT
% The most computationally intensive part of this function may be executed
% in parallel. The code sutable to be executed in parallel on multi core or cluster machine,
% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
% Then the DYNARE parallel package contain a set of pairs matlab functios that can be executed in
% parallel and called name_function.m and name_function_core.m.
% In addition in the parallel package we have second set of functions used
% to manage the parallel computation.
%
% This function was the first function to be parallelized, later other
% functions have been parallelized using the same methodology.
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management funtions.
% See the comments in the random_walk_metropolis_hastings.m funtion.
% Copyright (C) 2006-2013 Dynare Team
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -74,11 +63,9 @@ end
TargetFun=myinputs.TargetFun;
ProposalFun=myinputs.ProposalFun;
xparam1=myinputs.xparam1;
vv=myinputs.vv;
mh_bounds=myinputs.mh_bounds;
ix2=myinputs.ix2;
ilogpo2=myinputs.ilogpo2;
ModelName=myinputs.ModelName;
last_draw=myinputs.ix2;
last_posterior=myinputs.ilogpo2;
fline=myinputs.fline;
npar=myinputs.npar;
nruns=myinputs.nruns;
@ -94,11 +81,9 @@ estim_params_ = myinputs.estim_params_;
options_ = myinputs.options_;
M_ = myinputs.M_;
oo_ = myinputs.oo_;
varargin=myinputs.varargin;
% Necessary only for remote computing!
if whoiam
Parallel=myinputs.Parallel;
% initialize persistent variables in priordens()
priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, bayestopt_.p3,bayestopt_.p4,1);
end
@ -116,53 +101,51 @@ elseif strcmpi(ProposalFun,'rand_multivariate_student')
end
%
% NOW i run the (nblck-fblck+1) metropolis-hastings chains
% Now I run the (nblck-fblck+1) Metropolis-Hastings chains
%
proposal_covariance_Cholesky_decomposition = d*diag(bayestopt_.jscale);
jloop=0;
block_iter=0;
JSUM = 0;
for b = fblck:nblck,
jloop=jloop+1;
for curr_block = fblck:nblck,
block_iter=block_iter+1;
try
% This will not work if the master uses a random generator not
% This will not work if the master uses a random number generator not
% available in the slave (different Matlab version or
% Matlab/Octave cluster). Therefor the trap.
% Matlab/Octave cluster). Therefore the trap.
%
% This set the random generator type (the seed is useless but
% needed by the function)
% Set the random number generator type (the seed is useless but needed by the function)
set_dynare_seed(options_.DynareRandomStreams.algo, options_.DynareRandomStreams.seed);
% This set the state
set_dynare_random_generator_state(record.InitialSeeds(b).Unifor, record.InitialSeeds(b).Normal);
% Set the state of the RNG
set_dynare_random_generator_state(record.InitialSeeds(curr_block).Unifor, record.InitialSeeds(curr_block).Normal);
catch
% If the state set by master is incompatible with the slave, we
% only reseed
set_dynare_seed(options_.DynareRandomStreams.seed+b);
% If the state set by master is incompatible with the slave, we only reseed
set_dynare_seed(options_.DynareRandomStreams.seed+curr_block);
end
if (options_.load_mh_file~=0) && (fline(b)>1) && OpenOldFile(b)
load([BaseName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'])
x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
OpenOldFile(b) = 0;
if (options_.load_mh_file~=0) && (fline(curr_block)>1) && OpenOldFile(curr_block) %load previous draws and likelihood
load([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'])
x2 = [x2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,npar)];
logpo2 = [logpo2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,1)];
OpenOldFile(curr_block) = 0;
else
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
x2 = zeros(InitSizeArray(curr_block),npar);
logpo2 = zeros(InitSizeArray(curr_block),1);
end
%Prepare waiting bars
if whoiam
prc0=(b-fblck)/(nblck-fblck+1)*(isoctave || options_.console_mode);
hh = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},['MH (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
prc0=(curr_block-fblck)/(nblck-fblck+1)*(isoctave || options_.console_mode);
hh = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},['MH (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
else
hh = dyn_waitbar(0,['Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
hh = dyn_waitbar(0,['Metropolis-Hastings (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
set(hh,'Name','Metropolis-Hastings');
end
isux = 0;
jsux = 0;
irun = fline(b);
j = 1;
while j <= nruns(b)
par = feval(ProposalFun, ix2(b,:), proposal_covariance_Cholesky_decomposition, n);
accepted_draws_this_chain = 0;
accepted_draws_this_file = 0;
draw_index_current_file = fline(curr_block); %get location of first draw in current block
draw_iter = 1;
while draw_iter <= nruns(curr_block)
par = feval(ProposalFun, last_draw(curr_block,:), proposal_covariance_Cholesky_decomposition, n);
if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
try
logpost = - feval(TargetFun, par(:),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
@ -172,29 +155,29 @@ for b = fblck:nblck,
else
logpost = -inf;
end
if (logpost > -inf) && (log(rand) < logpost-ilogpo2(b))
x2(irun,:) = par;
ix2(b,:) = par;
logpo2(irun) = logpost;
ilogpo2(b) = logpost;
isux = isux + 1;
jsux = jsux + 1;
if (logpost > -inf) && (log(rand) < logpost-last_posterior(curr_block))
x2(draw_index_current_file,:) = par;
last_draw(curr_block,:) = par;
logpo2(draw_index_current_file) = logpost;
last_posterior(curr_block) = logpost;
accepted_draws_this_chain = accepted_draws_this_chain + 1;
accepted_draws_this_file = accepted_draws_this_file + 1;
else
x2(irun,:) = ix2(b,:);
logpo2(irun) = ilogpo2(b);
x2(draw_index_current_file,:) = last_draw(curr_block,:);
logpo2(draw_index_current_file) = last_posterior(curr_block);
end
prtfrc = j/nruns(b);
if (mod(j, 3)==0 && ~whoiam) || (mod(j,50)==0 && whoiam)
dyn_waitbar(prtfrc,hh,[ 'MH (' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('Current acceptance ratio %4.3f', isux/j)]);
prtfrc = draw_iter/nruns(curr_block);
if (mod(draw_iter, 3)==0 && ~whoiam) || (mod(draw_iter,50)==0 && whoiam)
dyn_waitbar(prtfrc,hh,[ 'MH (' int2str(curr_block) '/' int2str(options_.mh_nblck) ') ' sprintf('Current acceptance ratio %4.3f', accepted_draws_this_chain/draw_iter)]);
end
if (irun == InitSizeArray(b)) || (j == nruns(b)) % Now I save the simulations
save([BaseName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
if (draw_index_current_file == InitSizeArray(curr_block)) || (draw_iter == nruns(curr_block)) % Now I save the simulations, either because the current file is full or the chain is done
save([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'],'x2','logpo2');
fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
fprintf(fidlog,['\n']);
fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' datestr(now,0) ')\n']);
fprintf(fidlog,['%% Mh' int2str(NewFile(curr_block)) 'Blck' int2str(curr_block) ' (' datestr(now,0) ')\n']);
fprintf(fidlog,' \n');
fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
fprintf(fidlog,[' Acceptance ratio......: ' num2str(jsux/length(logpo2)) '\n']);
fprintf(fidlog,[' Acceptance ratio......: ' num2str(accepted_draws_this_file/length(logpo2)) '\n']);
fprintf(fidlog,[' Posterior mean........:\n']);
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
@ -212,32 +195,32 @@ for b = fblck:nblck,
fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
fprintf(fidlog,' \n');
fclose(fidlog);
jsux = 0;
if j == nruns(b) % I record the last draw...
record.LastParameters(b,:) = x2(end,:);
record.LastLogPost(b) = logpo2(end);
accepted_draws_this_file = 0;
if draw_iter == nruns(curr_block) % I record the last draw...
record.LastParameters(curr_block,:) = x2(end,:);
record.LastLogPost(curr_block) = logpo2(end);
end
% size of next file in chain b
InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
% size of next file in chain curr_block
InitSizeArray(curr_block) = min(nruns(curr_block)-draw_iter,MAX_nruns);
% initialization of next file if necessary
if InitSizeArray(b)
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
NewFile(b) = NewFile(b) + 1;
irun = 0;
if InitSizeArray(curr_block)
x2 = zeros(InitSizeArray(curr_block),npar);
logpo2 = zeros(InitSizeArray(curr_block),1);
NewFile(curr_block) = NewFile(curr_block) + 1;
draw_index_current_file = 0;
end
end
j=j+1;
irun = irun + 1;
draw_iter=draw_iter+1;
draw_index_current_file = draw_index_current_file + 1;
end% End of the simulations for one mh-block.
record.AcceptanceRatio(b) = isux/j;
record.AcceptanceRatio(curr_block) = accepted_draws_this_chain/draw_iter;
dyn_waitbar_close(hh);
[record.LastSeeds(b).Unifor, record.LastSeeds(b).Normal] = get_dynare_random_generator_state();
OutputFileName(jloop,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
[record.LastSeeds(curr_block).Unifor, record.LastSeeds(curr_block).Normal] = get_dynare_random_generator_state();
OutputFileName(block_iter,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(curr_block) '.mat']};
end% End of the loop over the mh-blocks.
myoutput.record = record;
myoutput.irun = irun;
myoutput.irun = draw_index_current_file;
myoutput.NewFile = NewFile;
myoutput.OutputFileName = OutputFileName;

32
matlab/trace_plot.m
View File

@ -1,13 +1,14 @@
function trace_plot(options_,M_,estim_params_,type,blck,name1,name2)
% This function build trace plot for the metropolis hastings draws.
%
% This function builds trace plot for the Metropolis-Hastings draws.
%
% INPUTS
%
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
% estim_params_ [structure] Dynare structure (related to estimation).
% type [string] 'DeepParameter', 'MeasurementError' (for measurement equation error) or 'StructuralShock' (for structural shock).
% type [string] 'DeepParameter', 'MeasurementError' (for measurement equation error),
% 'StructuralShock' (for structural shock)
% or 'PosteriorDensity (for posterior density)'
% blck [integer] Number of the mh chain.
% name1 [string] Object name.
% name2 [string] Object name.
@ -17,7 +18,7 @@ function trace_plot(options_,M_,estim_params_,type,blck,name1,name2)
%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2013 Dynare Team
% Copyright (C) 2003-2015 Dynare Team
%
% This file is part of Dynare.
%
@ -35,10 +36,15 @@ function trace_plot(options_,M_,estim_params_,type,blck,name1,name2)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Cet the column index:
if nargin<7
column = name2index(options_, M_, estim_params_, type, name1);
if strcmpi(type,'PosteriorDensity')
column=0;
name1='';
else
column = name2index(options_, M_, estim_params_, type, name1, name2);
if nargin<7
column = name2index(options_, M_, estim_params_, type, name1);
else
column = name2index(options_, M_, estim_params_, type, name1, name2);
end
end
if isempty(column)
@ -75,6 +81,8 @@ elseif strcmpi(type,'MeasurementError')
else
TYPE = 'the correlation between measurement errors ';
end
elseif strcmpi(type,'PosteriorDensity')
TYPE='the posterior density';
end
if nargin<7
@ -111,9 +119,15 @@ legend({'MCMC draw';[num2str(N) ' period moving average']},'Location','NorthWest
if ~exist(M_.fname, 'dir')
mkdir('.',M_.fname);
end
if ~exist([M_.fname filesep 'graphs'])
if ~exist([M_.fname filesep 'graphs'],'dir')
mkdir(M_.fname,'graphs');
end
plot_name=get_the_name(column,0,M_,estim_params_,options_);
%get name for plot
if strcmpi(type,'PosteriorDensity')
plot_name='Posterior';
else
plot_name=get_the_name(column,0,M_,estim_params_,options_);
end
dyn_saveas(hh,[M_.fname, filesep, 'graphs', filesep, 'TracePlot_' plot_name],options_)

26
matlab/utilities/general/delete_stale_file.m Normal file
View File

@ -0,0 +1,26 @@
function delete_stale_file(fname)
% function delete_old_files(fname)
% Checks for presence of files and deletes them if necessary
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
Files_info = dir(fname);
if length(Files_info)>0
delete(fname)
end

110
matlab/utilities/general/dyn_mex.m Normal file
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@ -0,0 +1,110 @@
function dyn_mex(win_compiler,basename,force)
% Compile Dynare model dlls when model option use_dll is used
% if C file is fresher than mex file
%
% INPUTS
% o win_compiler str compiler used under Windows (unused under Linux or OSX):
% 'msvc' (MS Visual C)
% 'cygwin'
% o basename str filenames base
% o force bool recompile if 1
%
% OUTPUTS
% none
%
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
Dc = dir([basename '_dynamic.c']);
Dmex = dir([basename '_dynamic.' mexext]);
% compile only if date of C file is greater than date of mex file
% and force is not True
if ~isempty(Dmex)
if (Dmex.datenum > Dc.datenum) && ~force
disp('Mex files are newer than the source: not recompiled')
return
end
end
if ~exist('OCTAVE_VERSION')
% Some mex commands are enclosed in an eval(), because otherwise it will make Octave fail
if ispc
if msvc
% MATLAB/Windows + Microsoft Visual C++
eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_static.c ' basename '_static_mex.c'])
elseif cygwin
% MATLAB/Windows + Cygwin g++
eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
basename '_static.c ' basename '_static_mex.c'])
else
error(['When using the USE_DLL option, you must give either ' ...
'''cygwin'' or ''msvc'' option to the ''dynare'' command'])
end
elseif isunix
% MATLAB/Linux
if matlab_ver_less_than('8.3')
eval(['mex -O LDFLAGS=''-pthread -shared -Wl,--no-undefined'' ' ...
basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O LDFLAGS=''-pthread -shared -Wl,--no-undefined'' ' ...
basename '_static.c ' basename '_static_mex.c'])
else
eval(['mex -O LINKEXPORT='''' ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O LINKEXPORT='''' ' basename '_static.c ' basename '_static_mex.c'])
end
elseif ismac
% MATLAB/MacOS
if matlab_ver_less_than('8.3')
if matlab_ver_less_than('8.1')
eval(['mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined ' ...
'error -arch $ARCHS -Wl,-syslibroot,$SDKROOT ' ...
'-mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle'' ' ...
basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined ' ...
'error -arch $ARCHS -Wl,-syslibroot,$SDKROOT ' ...
'-mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle'' ' ...
basename '_static.c ' basename '_static_mex.c'])
else
eval(['mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined ' ...
'error -arch $ARCHS -Wl,-syslibroot,$MW_SDKROOT ' ...
'-mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle'' ' ...
basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined ' ...
'error -arch $ARCHS -Wl,-syslibroot,$MW_SDKROOT ' ...
'-mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle'' ' ...
basename '_static.c ' basename '_static_mex.c'])
end
else
eval(['mex -O LINKEXPORT='''' ' basename '_dynamic.c ' ...
basename '_dynamic_mex.c'])
eval(['mex -O LINKEXPORT='''' ' basename '_static.c ' basename ...
'_static_mex.c'])
end
end
else
% Octave
eval(['mex ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
eval(['mex ' basename '_static.c ' basename '_static_mex.c'])
end

24
matlab/variance_decomposition_mc_analysis.m
View File

@ -1,6 +1,28 @@
function oo_ = variance_decomposition_mc_analysis(NumberOfSimulations,type,dname,fname,exonames,exo,vartan,var,mh_conf_sig,oo_)
% function oo_ = variance_decomposition_mc_analysis(NumberOfSimulations,type,dname,fname,exonames,exo,vartan,var,mh_conf_sig,oo_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variance decomposition.
% endogenous variables' variance decomposition.
%
% INPUTS
% NumberOfSimulations [integer] scalar, number of simulations.
% type [string] 'prior' or 'posterior'
% dname [string] directory name where to save
% fname [string] name of the mod-file
% exonames [string] (n_exo*char_length) character array with names of exogenous variables
% exo [string] name of current exogenous
% variable
% vartan [string] (n_endo*char_length) character array with name
% of endogenous variables
% var [integer] index of the current
% endogenous variable
% mh_conf_sig [double] 2 by 1 vector with upper
% and lower bound of HPD intervals
% oo_ [structure] Dynare structure where the results are saved.
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
% Copyright (C) 2008-2013 Dynare Team
%

63
matlab/ver_greater_than.m Normal file
View File

@ -0,0 +1,63 @@
function tf = ver_greater_than(ver1, ver2)
%function tf = ver_greater_than(ver1, ver2)
% ver1 > ver2 ? 1 : 0;
%
% INPUTS
% ver1 [string] software version number
% ver2 [string] software version number
%
% OUTPUTS
% tf [bool] true if ver1 > ver2
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
tf = true;
ver1 = strsplit(ver1, {'.', '-'});
ver2 = strsplit(ver2, {'.', '-'});
maj_ver1 = str2double(ver1{1});
maj_ver2 = str2double(ver2{1});
if maj_ver1 > maj_ver2
return;
end
min_ver1 = str2double(ver1{2});
min_ver2 = str2double(ver2{2});
if (maj_ver1 == maj_ver2) && (min_ver1 > min_ver2)
return;
end
ismaster1 = isnan(str2double(ver1{3}));
ismaster2 = isnan(str2double(ver2{3}));
if (maj_ver1 == maj_ver2) && (min_ver1 == min_ver2) && (ismaster1 && ~ismaster2)
return;
end
if ~ismaster1 && ~ismaster2
rev_ver1 = str2double(ver1{3});
rev_ver2 = str2double(ver2{3});
if (maj_ver1 == maj_ver2) && (min_ver1 == min_ver2) && (rev_ver1 > rev_ver2)
return;
end
end
tf = false;
end

33
matlab/ver_greater_than_equal.m Normal file
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@ -0,0 +1,33 @@
function tf = ver_greater_than_equal(ver1, ver2)
%function tf = ver_greater_than_equal(ver1, ver2)
% ver1 >= ver2 ? 1 : 0;
%
% INPUTS
% ver1 [string] software version number
% ver2 [string] software version number
%
% OUTPUTS
% tf [bool] true if ver1 > ver2
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
tf = ver_greater_than(ver1, ver2) || strcmp(ver1, ver2);
end

63
matlab/ver_less_than.m Normal file
View File

@ -0,0 +1,63 @@
function tf = ver_less_than(ver1, ver2)
%function tf = ver_less_than(ver1, ver2)
% ver1 < ver2 ? 1 : 0;
%
% INPUTS
% ver1 [string] software version number
% ver2 [string] software version number
%
% OUTPUTS
% tf [bool] true if ver1 < ver2
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
tf = true;
ver1 = strsplit(ver1, {'.', '-'});
ver2 = strsplit(ver2, {'.', '-'});
maj_ver1 = str2double(ver1{1});
maj_ver2 = str2double(ver2{1});
if maj_ver1 < maj_ver2
return;
end
min_ver1 = str2double(ver1{2});
min_ver2 = str2double(ver2{2});
if (maj_ver1 == maj_ver2) && (min_ver1 < min_ver2)
return;
end
ismaster1 = isnan(str2double(ver1{3}));
ismaster2 = isnan(str2double(ver2{3}));
if (maj_ver1 == maj_ver2) && (min_ver1 == min_ver2) && (~ismaster1 && ismaster2)
return;
end
if ~ismaster1 && ~ismaster2
rev_ver1 = str2double(ver1{3});
rev_ver2 = str2double(ver2{3});
if (maj_ver1 == maj_ver2) && (min_ver1 == min_ver2) && (rev_ver1 < rev_ver2)
return;
end
end
tf = false;
end

204
preprocessor/DynamicModel.cc
View File

@ -261,8 +261,6 @@ DynamicModel::writeModelEquationsOrdered_M(const string &dynamic_basename) const
{
int lag = it->first.first;
unsigned int var = it->first.second.first;
//int eqr = getBlockInitialEquationID(block, eq);
//int varr = getBlockInitialVariableID(block, var);
if (var != prev_var || lag != prev_lag)
{
prev_var = var;
@ -280,8 +278,6 @@ DynamicModel::writeModelEquationsOrdered_M(const string &dynamic_basename) const
{
int lag = it->first.first;
unsigned int var = it->first.second.first;
//int eqr = getBlockInitialEquationID(block, eq);
//int varr = getBlockInitialVariableID(block, var);
if (var != prev_var || lag != prev_lag)
{
prev_var = var;
@ -299,8 +295,6 @@ DynamicModel::writeModelEquationsOrdered_M(const string &dynamic_basename) const
{
int lag = it->first.first;
unsigned int var = it->first.second.first;
//int eqr = getBlockInitialEquationID(block, eq);
//int varr = getBlockInitialVariableID(block, var);
if (var != prev_var || lag != prev_lag)
{
prev_var = var;
@ -318,8 +312,6 @@ DynamicModel::writeModelEquationsOrdered_M(const string &dynamic_basename) const
{
int lag = it->first.first;
unsigned int var = it->first.second.first;
//int eqr = getBlockInitialEquationID(block, eq);
//int varr = getBlockInitialVariableID(block, var);
if (var != prev_var || lag != prev_lag)
{
prev_var = var;
@ -2801,89 +2793,6 @@ DynamicModel::writeOutput(ostream &output, const string &basename, bool block_de
typedef pair<int, pair<int, int > > index_KF;
vector<index_KF> v_index_KF;
/* DO 170, J = 1, N
TEMP1 = ALPHA*A( J, J )
DO 110, I = 1, M
C( I, J ) = TEMP1*B( I, J )
11 110 CONTINUE
DO 140, K = 1, J - 1
TEMP1 = ALPHA*A( K, J )
DO 130, I = 1, M
C( I, J ) = C( I, J ) + TEMP1*B( I, K )
13 130 CONTINUE
14 140 CONTINUE
DO 160, K = J + 1, N
TEMP1 = ALPHA*A( J, K )
DO 150, I = 1, M
C( I, J ) = C( I, J ) + TEMP1*B( I, K )
15 150 CONTINUE
16 160 CONTINUE
17 170 CONTINUE
for(int j = 0; j < n; j++)
{
double temp1 = P_t_t1[j + j * n];
for (int i = 0; i < n; i++)
tmp[i + j * n] = tmp1 * T[i + j * n];
for (int k = 0; k < j - 1; k++)
{
temp1 = P_t_t1[k + j * n];
for (int i = 0; i < n; i++)
tmp[i + j * n] += temp1 * T[i + k * n];
}
for (int k = j + 1; k < n; k++)
{
temp1 = P_t_t1[j + k * n];
for (int i = 0; i < n; i++)
tmp[i + j * n] += temp1 * T[i + k * n];
}
}
for(int j = n_obs; j < n; j++)
{
int j1 = j - n_obs;
double temp1 = P_t_t1[j1 + j1 * n_state];
for (int i = 0; i < n; i++)
tmp[i + j1 * n] = tmp1 * T[i + j * n];
for (int k = n_obs; k < j - 1; k++)
{
int k1 = k - n_obs;
temp1 = P_t_t1[k1 + j1 * n_state];
for (int i = 0; i < n; i++)
tmp[i + j1 * n] += temp1 * T[i + k * n];
}
for (int k = max(j + 1, n_obs); k < n; k++)
{
int k1 = k - n_obs;
temp1 = P_t_t1[j1 + k1 * n_state];
for (int i = 0; i < n; i++)
tmp[i + j1 * n] += temp1 * T[i + k * n];
}
}
for(int j = n_obs; j < n; j++)
{
int j1 = j - n_obs;
double temp1 = P_t_t1[j1 + j1 * n_state];
for (int i = 0; i < n; i++)
tmp[i + j1 * n] = tmp1 * T[i + j * n];
for (int k = n_obs; k < j - 1; k++)
{
int k1 = k - n_obs;
temp1 = P_t_t1[k1 + j1 * n_state];
for (int i = 0; i < n; i++)
if ((i < n_obs) || (i >= nb_diag + n_obs) || (j1 >= nb_diag))
tmp[i + j1 * n] += temp1 * T[i + k * n];
}
for (int k = max(j + 1, n_obs); k < n; k++)
{
int k1 = k - n_obs;
temp1 = P_t_t1[j1 + k1 * n_state];
for (int i = 0; i < n; i++)
if ((i < n_obs) || (i >= nb_diag + n_obs) || (j1 >= nb_diag))
tmp[i + j1 * n] += temp1 * T[i + k * n];
}
}*/
for (int i = 0; i < n; i++)
//int i = 0;
for (int j = n_obs; j < n; j++)
@ -2902,7 +2811,6 @@ DynamicModel::writeOutput(ostream &output, const string &basename, bool block_de
for (vector<index_KF>::iterator it = v_index_KF.begin(); it != v_index_KF.end(); it++)
KF_index_file.write(reinterpret_cast<char *>(&(*it)), sizeof(index_KF));
//typedef pair<pair<int, int>, pair<int, int > > index_KF_2;
vector<index_KF> v_index_KF_2;
int n_n_obs = n * n_obs;
for (int i = 0; i < n; i++)
@ -4322,5 +4230,117 @@ DynamicModel::dynamicOnlyEquationsNbr() const
return eqs.size();
}
#ifndef PRIVATE_BUFFER_SIZE
#define PRIVATE_BUFFER_SIZE 1024
#endif
bool
DynamicModel::isChecksumMatching(const string &basename) const
{
boost::crc_32_type result;
std::stringstream buffer;
// Write equation tags
for (size_t i = 0; i < equation_tags.size(); i++)
buffer << " " << equation_tags[i].first + 1
<< equation_tags[i].second.first
<< equation_tags[i].second.second;
ExprNodeOutputType buffer_type = oCDynamicModel;
for (int eq = 0; eq < (int) equations.size(); eq++)
{
BinaryOpNode *eq_node = equations[eq];
expr_t lhs = eq_node->get_arg1();
expr_t rhs = eq_node->get_arg2();
// Test if the right hand side of the equation is empty.
double vrhs = 1.0;
try
{
vrhs = rhs->eval(eval_context_t());
}
catch (ExprNode::EvalException &e)
{
}
if (vrhs != 0) // The right hand side of the equation is not empty ==> residual=lhs-rhs;
{
buffer << "lhs =";
lhs->writeOutput(buffer, buffer_type, temporary_terms);
buffer << ";" << endl;
buffer << "rhs =";
rhs->writeOutput(buffer, buffer_type, temporary_terms);
buffer << ";" << endl;
buffer << "residual" << LEFT_ARRAY_SUBSCRIPT(buffer_type)
<< eq + ARRAY_SUBSCRIPT_OFFSET(buffer_type)
<< RIGHT_ARRAY_SUBSCRIPT(buffer_type)
<< "= lhs-rhs;" << endl;
}
else // The right hand side of the equation is empty ==> residual=lhs;
{
buffer << "residual" << LEFT_ARRAY_SUBSCRIPT(buffer_type)
<< eq + ARRAY_SUBSCRIPT_OFFSET(buffer_type)
<< RIGHT_ARRAY_SUBSCRIPT(buffer_type)
<< " = ";
lhs->writeOutput(buffer, buffer_type, temporary_terms);
buffer << ";" << endl;
}
}
char private_buffer[PRIVATE_BUFFER_SIZE];
while(buffer)
{
buffer.get(private_buffer,PRIVATE_BUFFER_SIZE);
result.process_bytes(private_buffer,strlen(private_buffer));
}
bool basename_dir_exists = false;
#ifdef _WIN32
int r = mkdir(basename.c_str());
#else
int r = mkdir(basename.c_str(), 0777);
#endif
if (r < 0)
if (errno != EEXIST)
{
perror("ERROR");
exit(EXIT_FAILURE);
}
else
basename_dir_exists = true;
// check whether basename directory exist. If not, create it.
// If it does, read old checksum if it exist
fstream checksum_file;
string filename = basename + "/checksum";
unsigned int old_checksum = 0;
// read old checksum if it exists
if (basename_dir_exists)
{
checksum_file.open(filename.c_str(), ios::in | ios::binary);
if (checksum_file.is_open())
{
checksum_file >> old_checksum;
checksum_file.close();
}
}
// write new checksum file if none or different from old checksum
if (old_checksum != result.checksum())
{
checksum_file.open(filename.c_str(), ios::out | ios::binary);
if (!checksum_file.is_open())
{
cerr << "ERROR: Can't open file " << filename << endl;
exit(EXIT_FAILURE);
}
checksum_file << result.checksum();
checksum_file.close();
return false;
}
return true;
}

3
preprocessor/DynamicModel.hh
View File

@ -24,6 +24,7 @@ using namespace std;
#define ZERO_BAND 1e-8
#include <fstream>
#include <boost/crc.hpp>
#include "StaticModel.hh"
@ -482,6 +483,8 @@ public:
void writeSecondDerivativesC_csr(const string &basename, bool cuda) const;
//! Writes C file containing third order derivatives of model evaluated at steady state (compressed sparse column)
void writeThirdDerivativesC_csr(const string &basename, bool cuda) const;
bool isChecksumMatching(const string &basename) const;
};
//! Classes to re-order derivatives for various sparse storage formats

36
preprocessor/DynareBison.yy
View File

@ -82,17 +82,17 @@ class ParsingDriver;
#define yylex driver.lexer->lex
}
%token AIM_SOLVER ANALYTIC_DERIVATION AR AUTOCORR
%token BAYESIAN_IRF BETA_PDF BLOCK USE_CALIBRATION
%token BVAR_DENSITY BVAR_FORECAST NODECOMPOSITION
%token BVAR_PRIOR_DECAY BVAR_PRIOR_FLAT BVAR_PRIOR_LAMBDA
%token AIM_SOLVER ANALYTIC_DERIVATION AR AUTOCORR TARB_MODE_COMPUTE
%token BAYESIAN_IRF BETA_PDF BLOCK USE_CALIBRATION USE_TARB TARB_NEW_BLOCK_PROBABILITY
%token BVAR_DENSITY BVAR_FORECAST NODECOMPOSITION DR_DISPLAY_TOL HUGE_NUMBER
%token BVAR_PRIOR_DECAY BVAR_PRIOR_FLAT BVAR_PRIOR_LAMBDA TARB_OPTIM
%token BVAR_PRIOR_MU BVAR_PRIOR_OMEGA BVAR_PRIOR_TAU BVAR_PRIOR_TRAIN
%token BVAR_REPLIC BYTECODE ALL_VALUES_REQUIRED
%token CALIB_SMOOTHER CHANGE_TYPE CHECK CONDITIONAL_FORECAST CONDITIONAL_FORECAST_PATHS CONF_SIG CONSTANT CONTROLLED_VAREXO CORR COVAR CUTOFF CYCLE_REDUCTION LOGARITHMIC_REDUCTION
%token CONSIDER_ALL_ENDOGENOUS CONSIDER_ONLY_OBSERVED
%token DATAFILE FILE SERIES DOUBLING DR_CYCLE_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_MAXITER DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE CALIBRATION DIFFERENTIATE_FORWARD_VARS
%token END ENDVAL EQUAL ESTIMATION ESTIMATED_PARAMS ESTIMATED_PARAMS_BOUNDS ESTIMATED_PARAMS_INIT EXTENDED_PATH ENDOGENOUS_PRIOR
%token FILENAME FILTER_STEP_AHEAD FILTERED_VARS FIRST_OBS LAST_OBS SET_TIME
%token FILENAME DIRNAME FILTER_STEP_AHEAD FILTERED_VARS FIRST_OBS LAST_OBS SET_TIME
%token <string_val> FLOAT_NUMBER DATES
%token DEFAULT FIXED_POINT OPT_ALGO
%token FORECAST K_ORDER_SOLVER INSTRUMENTS SHIFT MEAN STDEV VARIANCE MODE INTERVAL SHAPE DOMAINN
@ -1094,6 +1094,7 @@ stoch_simul_primary_options : o_dr_algo
| o_dr_logarithmic_reduction_tol
| o_dr_logarithmic_reduction_maxiter
| o_irf_plot_threshold
| o_dr_display_tol
;
stoch_simul_options : stoch_simul_primary_options
@ -1712,6 +1713,12 @@ estimation_options : o_datafile
| o_filter_algorithm
| o_proposal_approximation
| o_distribution_approximation
| o_dirname
| o_huge_number
| o_use_tarb
| o_tarb_mode_compute
| o_tarb_new_block_probability
| o_tarb_optim
;
list_optim_option : QUOTED_STRING COMMA QUOTED_STRING
@ -1724,6 +1731,16 @@ optim_options : list_optim_option
| optim_options COMMA list_optim_option;
;
list_tarb_optim_option : QUOTED_STRING COMMA QUOTED_STRING
{ driver.tarb_optim_options_string($1, $3); }
| QUOTED_STRING COMMA signed_number
{ driver.tarb_optim_options_num($1, $3); }
;
tarb_optim_options : list_tarb_optim_option
| tarb_optim_options COMMA list_tarb_optim_option;
;
varobs : VAROBS { driver.check_varobs(); } varobs_list ';';
varobs_list : varobs_list symbol
@ -1768,6 +1785,7 @@ osr_options : stoch_simul_primary_options
| o_osr_tolf
| o_opt_algo
| o_optim
| o_huge_number
;
osr : OSR ';'
@ -2546,6 +2564,8 @@ o_qz_zero_threshold : QZ_ZERO_THRESHOLD EQUAL non_negative_number { driver.optio
o_file : FILE EQUAL filename { driver.option_str("file", $3); };
o_series : SERIES EQUAL symbol { driver.option_str("series", $3); };
o_datafile : DATAFILE EQUAL filename { driver.option_str("datafile", $3); };
o_dirname : DIRNAME EQUAL filename { driver.option_str("dirname", $3); };
o_huge_number : HUGE_NUMBER EQUAL non_negative_number { driver.option_num("huge_number", $3); };
o_nobs : NOBS EQUAL vec_int
{ driver.option_vec_int("nobs", $3); }
| NOBS EQUAL vec_int_number
@ -2612,6 +2632,7 @@ o_posterior_max_subsample_draws : POSTERIOR_MAX_SUBSAMPLE_DRAWS EQUAL INT_NUMBER
o_mh_drop : MH_DROP EQUAL non_negative_number { driver.option_num("mh_drop", $3); };
o_mh_jscale : MH_JSCALE EQUAL non_negative_number { driver.option_num("mh_jscale", $3); };
o_optim : OPTIM EQUAL '(' optim_options ')';
o_tarb_optim : TARB_OPTIM EQUAL '(' tarb_optim_options ')';
o_mh_init_scale : MH_INIT_SCALE EQUAL non_negative_number { driver.option_num("mh_init_scale", $3); };
o_mode_file : MODE_FILE EQUAL filename { driver.option_str("mode_file", $3); };
o_mode_compute : MODE_COMPUTE EQUAL INT_NUMBER { driver.option_num("mode_compute", $3); };
@ -2866,7 +2887,9 @@ o_equations : EQUATIONS EQUAL vec_int
| EQUATIONS EQUAL vec_int_number
{ driver.option_vec_int("ms.equations",$3); }
;
o_use_tarb : USE_TARB { driver.option_num("TaRB.use_TaRB", "1"); };
o_tarb_mode_compute : TARB_MODE_COMPUTE EQUAL INT_NUMBER { driver.option_num("TaRB.mode_compute", $3); };
o_tarb_new_block_probability : TARB_NEW_BLOCK_PROBABILITY EQUAL non_negative_number {driver.option_num("TaRB.new_block_probability",$3); };
o_instruments : INSTRUMENTS EQUAL '(' symbol_list ')' {driver.option_symbol_list("instruments"); };
o_ext_func_name : EXT_FUNC_NAME EQUAL filename { driver.external_function_option("name", $3); };
@ -2964,6 +2987,7 @@ o_mcmc_jumping_covariance : MCMC_JUMPING_COVARIANCE EQUAL HESSIAN
{ driver.option_str("MCMC_jumping_covariance", $3); }
;
o_irf_plot_threshold : IRF_PLOT_THRESHOLD EQUAL non_negative_number { driver.option_num("impulse_responses.plot_threshold", $3); };
o_dr_display_tol : DR_DISPLAY_TOL EQUAL non_negative_number { driver.option_num("dr_display_tol", $3); };
o_consider_all_endogenous : CONSIDER_ALL_ENDOGENOUS { driver.option_str("endo_vars_for_moment_computations_in_estimation", "all_endogenous_variables"); };
o_consider_only_observed : CONSIDER_ONLY_OBSERVED { driver.option_str("endo_vars_for_moment_computations_in_estimation", "only_observed_variables"); };
o_no_homotopy : NO_HOMOTOPY { driver.option_num("no_homotopy", "1"); };

7
preprocessor/DynareFlex.ll
View File

@ -236,6 +236,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
<DYNARE_STATEMENT>dates {dates_parens_nb=0; BEGIN DATES_STATEMENT; yylval->string_val = new string("dates");}
<DYNARE_STATEMENT>file {return token::FILE;}
<DYNARE_STATEMENT>datafile {return token::DATAFILE;}
<DYNARE_STATEMENT>dirname {return token::DIRNAME;}
<DYNARE_STATEMENT>nobs {return token::NOBS;}
<DYNARE_STATEMENT>last_obs {return token::LAST_OBS;}
<DYNARE_STATEMENT>first_obs {return token::FIRST_OBS;}
@ -566,6 +567,12 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
<DYNARE_STATEMENT>period {return token::PERIOD;}
<DYNARE_STATEMENT>outfile {return token::OUTFILE;}
<DYNARE_STATEMENT>outvars {return token::OUTVARS;}
<DYNARE_STATEMENT>huge_number {return token::HUGE_NUMBER;}
<DYNARE_STATEMENT>dr_display_tol {return token::DR_DISPLAY_TOL;}
<DYNARE_STATEMENT>use_tarb {return token::USE_TARB;}
<DYNARE_STATEMENT>tarb_mode_compute {return token::TARB_MODE_COMPUTE;}
<DYNARE_STATEMENT>tarb_new_block_probability {return token::TARB_NEW_BLOCK_PROBABILITY;}
<DYNARE_STATEMENT>tarb_optim {return token::TARB_OPTIM;}
<DYNARE_STATEMENT>[\$][^$]*[\$] {
strtok(yytext+1, "$");

9
preprocessor/DynareMain.cc
View File

@ -38,7 +38,7 @@
void main2(stringstream &in, string &basename, bool debug, bool clear_all, bool clear_global, bool no_tmp_terms, bool no_log, bool no_warn,
bool warn_uninit, bool console, bool nograph, bool nointeractive,
bool parallel, const string &parallel_config_file, const string &cluster_name, bool parallel_slave_open_mode,
bool parallel_test, bool nostrict, FileOutputType output_mode, LanguageOutputType lang
bool parallel_test, bool nostrict, bool check_model_changes, FileOutputType output_mode, LanguageOutputType lang
#if defined(_WIN32) || defined(__CYGWIN32__)
, bool cygwin, bool msvc
#endif
@ -49,7 +49,7 @@ usage()
{
cerr << "Dynare usage: dynare mod_file [debug] [noclearall] [onlyclearglobals] [savemacro[=macro_file]] [onlymacro] [nolinemacro] [notmpterms] [nolog] [warn_uninit]"
<< " [console] [nograph] [nointeractive] [parallel[=cluster_name]] [conffile=parallel_config_path_and_filename] [parallel_slave_open_mode] [parallel_test] "
<< " [-D<variable>[=<value>]] [nostrict] [output=dynamic|first|second|third] [language=C|C++]"
<< " [-D<variable>[=<value>]] [nostrict] [fast] [output=dynamic|first|second|third] [language=C|C++]"
#if defined(_WIN32) || defined(__CYGWIN32__)
<< " [cygwin] [msvc]"
#endif
@ -97,6 +97,7 @@ main(int argc, char **argv)
bool parallel_slave_open_mode = false;
bool parallel_test = false;
bool nostrict = false;
bool check_model_changes = false;
map<string, string> defines;
FileOutputType output_mode = none;
LanguageOutputType language = matlab;
@ -165,6 +166,8 @@ main(int argc, char **argv)
parallel_test = true;
else if (!strcmp(argv[arg], "nostrict"))
nostrict = true;
else if (!strcmp(argv[arg], "fast"))
check_model_changes = true;
else if (strlen(argv[arg]) >= 8 && !strncmp(argv[arg], "parallel", 8))
{
parallel = true;
@ -285,7 +288,7 @@ main(int argc, char **argv)
// Do the rest
main2(macro_output, basename, debug, clear_all, clear_global, no_tmp_terms, no_log, no_warn, warn_uninit, console, nograph, nointeractive,
parallel, parallel_config_file, cluster_name, parallel_slave_open_mode, parallel_test, nostrict, output_mode, language
parallel, parallel_config_file, cluster_name, parallel_slave_open_mode, parallel_test, nostrict, check_model_changes, output_mode, language
#if defined(_WIN32) || defined(__CYGWIN32__)
, cygwin, msvc
#endif

9
preprocessor/DynareMain2.cc
View File

@ -27,7 +27,7 @@
void
main2(stringstream &in, string &basename, bool debug, bool clear_all, bool clear_global, bool no_tmp_terms, bool no_log, bool no_warn, bool warn_uninit, bool console, bool nograph, bool nointeractive,
bool parallel, const string &parallel_config_file, const string &cluster_name, bool parallel_slave_open_mode,
bool parallel_test, bool nostrict, FileOutputType output_mode, LanguageOutputType language
bool parallel_test, bool nostrict, bool check_model_changes, FileOutputType output_mode, LanguageOutputType language
#if defined(_WIN32) || defined(__CYGWIN32__)
, bool cygwin, bool msvc
#endif
@ -60,11 +60,12 @@ main2(stringstream &in, string &basename, bool debug, bool clear_all, bool clear
if (output_mode != none)
mod_file->writeExternalFiles(basename, output_mode, language);
else
mod_file->writeOutputFiles(basename, clear_all, clear_global, no_log, no_warn, console, nograph, nointeractive, config_file
mod_file->writeOutputFiles(basename, clear_all, clear_global, no_log, no_warn, console, nograph,
nointeractive, config_file, check_model_changes
#if defined(_WIN32) || defined(__CYGWIN32__)
, cygwin, msvc
, cygwin, msvc
#endif
);
);
delete mod_file;

694
preprocessor/ExprNode.cc
View File

@ -308,6 +308,12 @@ NumConstNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
output << datatree.num_constants.get(id);
}
bool
NumConstNode::containsExternalFunction() const
{
return false;
}
double
NumConstNode::eval(const eval_context_t &eval_context) const throw (EvalException, EvalExternalFunctionException)
{
@ -577,6 +583,12 @@ VariableNode::collectTemporary_terms(const temporary_terms_t &temporary_terms, t
datatree.local_variables_table[symb_id]->collectTemporary_terms(temporary_terms, temporary_terms_inuse, Curr_Block);
}
bool
VariableNode::containsExternalFunction() const
{
return false;
}
void
VariableNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
@ -1726,6 +1738,12 @@ UnaryOpNode::collectTemporary_terms(const temporary_terms_t &temporary_terms, te
arg->collectTemporary_terms(temporary_terms, temporary_terms_inuse, Curr_Block);
}
bool
UnaryOpNode::containsExternalFunction() const
{
return arg->containsExternalFunction();
}
void
UnaryOpNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
@ -2904,6 +2922,13 @@ BinaryOpNode::collectTemporary_terms(const temporary_terms_t &temporary_terms, t
}
}
bool
BinaryOpNode::containsExternalFunction() const
{
return arg1->containsExternalFunction()
|| arg2->containsExternalFunction();
}
void
BinaryOpNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
@ -3990,6 +4015,14 @@ TrinaryOpNode::collectTemporary_terms(const temporary_terms_t &temporary_terms,
}
}
bool
TrinaryOpNode::containsExternalFunction() const
{
return arg1->containsExternalFunction()
|| arg2->containsExternalFunction()
|| arg3->containsExternalFunction();
}
void
TrinaryOpNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
@ -4333,15 +4366,6 @@ AbstractExternalFunctionNode::AbstractExternalFunctionNode(DataTree &datatree_ar
{
}
ExternalFunctionNode::ExternalFunctionNode(DataTree &datatree_arg,
int symb_id_arg,
const vector<expr_t> &arguments_arg) :
AbstractExternalFunctionNode(datatree_arg, symb_id_arg, arguments_arg)
{
// Add myself to the external function map
datatree.external_function_node_map[make_pair(arguments, symb_id)] = this;
}
void
AbstractExternalFunctionNode::prepareForDerivation()
{
@ -4372,20 +4396,6 @@ AbstractExternalFunctionNode::computeDerivative(int deriv_id)
return composeDerivatives(dargs);
}
expr_t
ExternalFunctionNode::composeDerivatives(const vector<expr_t> &dargs)
{
vector<expr_t> dNodes;
for (int i = 0; i < (int) dargs.size(); i++)
dNodes.push_back(datatree.AddTimes(dargs.at(i),
datatree.AddFirstDerivExternalFunction(symb_id, arguments, i+1)));
expr_t theDeriv = datatree.Zero;
for (vector<expr_t>::const_iterator it = dNodes.begin(); it != dNodes.end(); it++)
theDeriv = datatree.AddPlus(theDeriv, *it);
return theDeriv;
}
expr_t
AbstractExternalFunctionNode::getChainRuleDerivative(int deriv_id, const map<int, expr_t> &recursive_variables)
{
@ -4397,76 +4407,6 @@ AbstractExternalFunctionNode::getChainRuleDerivative(int deriv_id, const map<int
return composeDerivatives(dargs);
}
void
ExternalFunctionNode::computeTemporaryTerms(map<expr_t, int> &reference_count,
temporary_terms_t &temporary_terms,
bool is_matlab) const
{
temporary_terms.insert(const_cast<ExternalFunctionNode *>(this));
}
void
AbstractExternalFunctionNode::writeExternalFunctionArguments(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
if (it != arguments.begin())
output << ",";
(*it)->writeOutput(output, output_type, temporary_terms, tef_terms);
}
}
void
AbstractExternalFunctionNode::writePrhs(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms, const string &ending) const
{
output << "mxArray *prhs"<< ending << "[nrhs"<< ending << "];" << endl;
int i = 0;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
output << "prhs" << ending << "[" << i++ << "] = mxCreateDoubleScalar("; // All external_function arguments are scalars
(*it)->writeOutput(output, output_type, temporary_terms, tef_terms);
output << ");" << endl;
}
}
void
ExternalFunctionNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
if (output_type == oMatlabOutsideModel || output_type == oSteadyStateFile
|| output_type == oCSteadyStateFile || IS_LATEX(output_type))
{
string name = IS_LATEX(output_type) ? datatree.symbol_table.getTeXName(symb_id)
: datatree.symbol_table.getName(symb_id);
output << name << "(";
writeExternalFunctionArguments(output, output_type, temporary_terms, tef_terms);
output << ")";
return;
}
temporary_terms_t::const_iterator it = temporary_terms.find(const_cast<ExternalFunctionNode *>(this));
if (it != temporary_terms.end())
{
if (output_type == oMatlabDynamicModelSparse)
output << "T" << idx << "(it_)";
else
output << "T" << idx;
return;
}
if (IS_C(output_type))
output << "*";
output << "TEF_" << getIndxInTefTerms(symb_id, tef_terms);
}
unsigned int
AbstractExternalFunctionNode::compileExternalFunctionArguments(ostream &CompileCode, unsigned int &instruction_number,
bool lhs_rhs, const temporary_terms_t &temporary_terms,
@ -4480,184 +4420,6 @@ AbstractExternalFunctionNode::compileExternalFunctionArguments(ostream &CompileC
return (arguments.size());
}
void
ExternalFunctionNode::compile(ostream &CompileCode, unsigned int &instruction_number,
bool lhs_rhs, const temporary_terms_t &temporary_terms,
const map_idx_t &map_idx, bool dynamic, bool steady_dynamic,
deriv_node_temp_terms_t &tef_terms) const
{
temporary_terms_t::const_iterator it = temporary_terms.find(const_cast<ExternalFunctionNode *>(this));
if (it != temporary_terms.end())
{
if (dynamic)
{
map_idx_t::const_iterator ii = map_idx.find(idx);
FLDT_ fldt(ii->second);
fldt.write(CompileCode, instruction_number);
}
else
{
map_idx_t::const_iterator ii = map_idx.find(idx);
FLDST_ fldst(ii->second);
fldst.write(CompileCode, instruction_number);
}
return;
}
if (!lhs_rhs)
{
FLDTEF_ fldtef(getIndxInTefTerms(symb_id, tef_terms));
fldtef.write(CompileCode, instruction_number);
}
else
{
FSTPTEF_ fstptef(getIndxInTefTerms(symb_id, tef_terms));
fstptef.write(CompileCode, instruction_number);
}
}
void
ExternalFunctionNode::writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
int first_deriv_symb_id = datatree.external_functions_table.getFirstDerivSymbID(symb_id);
assert(first_deriv_symb_id != eExtFunSetButNoNameProvided);
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
(*it)->writeExternalFunctionOutput(output, output_type, temporary_terms, tef_terms);
if (!alreadyWrittenAsTefTerm(symb_id, tef_terms))
{
tef_terms[make_pair(symb_id, arguments)] = (int) tef_terms.size();
int indx = getIndxInTefTerms(symb_id, tef_terms);
int second_deriv_symb_id = datatree.external_functions_table.getSecondDerivSymbID(symb_id);
assert(second_deriv_symb_id != eExtFunSetButNoNameProvided);
if (IS_C(output_type))
{
stringstream ending;
ending << "_tef_" << getIndxInTefTerms(symb_id, tef_terms);
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "int nlhs" << ending.str() << " = 3;" << endl
<< "double *TEF_" << indx << ", "
<< "*TEFD_" << indx << ", "
<< "*TEFDD_" << indx << ";" << endl;
else if (symb_id == first_deriv_symb_id)
output << "int nlhs" << ending.str() << " = 2;" << endl
<< "double *TEF_" << indx << ", "
<< "*TEFD_" << indx << "; " << endl;
else
output << "int nlhs" << ending.str() << " = 1;" << endl
<< "double *TEF_" << indx << ";" << endl;
output << "mxArray *plhs" << ending.str()<< "[nlhs"<< ending.str() << "];" << endl;
output << "int nrhs" << ending.str()<< " = " << arguments.size() << ";" << endl;
writePrhs(output, output_type, temporary_terms, tef_terms, ending.str());
output << "mexCallMATLAB("
<< "nlhs" << ending.str() << ", "
<< "plhs" << ending.str() << ", "
<< "nrhs" << ending.str() << ", "
<< "prhs" << ending.str() << ", \""
<< datatree.symbol_table.getName(symb_id) << "\");" << endl;
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl
<< "TEFD_" << indx << " = mxGetPr(plhs" << ending.str() << "[1]);" << endl
<< "TEFDD_" << indx << " = mxGetPr(plhs" << ending.str() << "[2]);" << endl
<< "int TEFDD_" << indx << "_nrows = (int)mxGetM(plhs" << ending.str()<< "[2]);" << endl;
else if (symb_id == first_deriv_symb_id)
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl
<< "TEFD_" << indx << " = mxGetPr(plhs" << ending.str() << "[1]);" << endl;
else
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl;
}
else
{
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "[TEF_" << indx << " TEFD_"<< indx << " TEFDD_"<< indx << "] = ";
else if (symb_id == first_deriv_symb_id)
output << "[TEF_" << indx << " TEFD_"<< indx << "] = ";
else
output << "TEF_" << indx << " = ";
output << datatree.symbol_table.getName(symb_id) << "(";
writeExternalFunctionArguments(output, output_type, temporary_terms, tef_terms);
output << ");" << endl;
}
}
}
void
ExternalFunctionNode::compileExternalFunctionOutput(ostream &CompileCode, unsigned int &instruction_number,
bool lhs_rhs, const temporary_terms_t &temporary_terms,
const map_idx_t &map_idx, bool dynamic, bool steady_dynamic,
deriv_node_temp_terms_t &tef_terms) const
{
int first_deriv_symb_id = datatree.external_functions_table.getFirstDerivSymbID(symb_id);
assert(first_deriv_symb_id != eExtFunSetButNoNameProvided);
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
(*it)->compileExternalFunctionOutput(CompileCode, instruction_number, lhs_rhs, temporary_terms,
map_idx, dynamic, steady_dynamic, tef_terms);
if (!alreadyWrittenAsTefTerm(symb_id, tef_terms))
{
tef_terms[make_pair(symb_id, arguments)] = (int) tef_terms.size();
int indx = getIndxInTefTerms(symb_id, tef_terms);
int second_deriv_symb_id = datatree.external_functions_table.getSecondDerivSymbID(symb_id);
assert(second_deriv_symb_id != eExtFunSetButNoNameProvided);
unsigned int nb_output_arguments = 0;
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
nb_output_arguments = 3;
else if (symb_id == first_deriv_symb_id)
nb_output_arguments = 2;
else
nb_output_arguments = 1;
unsigned int nb_input_arguments = compileExternalFunctionArguments(CompileCode, instruction_number, lhs_rhs, temporary_terms,
map_idx, dynamic, steady_dynamic, tef_terms);
FCALL_ fcall(nb_output_arguments, nb_input_arguments, datatree.symbol_table.getName(symb_id), indx);
switch (nb_output_arguments)
{
case 1:
fcall.set_function_type(ExternalFunctionWithoutDerivative);
break;
case 2:
fcall.set_function_type(ExternalFunctionWithFirstDerivative);
break;
case 3:
fcall.set_function_type(ExternalFunctionWithFirstandSecondDerivative);
break;
}
fcall.write(CompileCode, instruction_number);
FSTPTEF_ fstptef(indx);
fstptef.write(CompileCode, instruction_number);
}
}
void
ExternalFunctionNode::computeTemporaryTerms(map<expr_t, int> &reference_count,
temporary_terms_t &temporary_terms,
map<expr_t, pair<int, int> > &first_occurence,
int Curr_block,
vector< vector<temporary_terms_t> > &v_temporary_terms,
int equation) const
{
expr_t this2 = const_cast<ExternalFunctionNode *>(this);
temporary_terms.insert(this2);
first_occurence[this2] = make_pair(Curr_block, equation);
v_temporary_terms[Curr_block][equation].insert(this2);
}
void
AbstractExternalFunctionNode::collectDynamicVariables(SymbolType type_arg, set<pair<int, int> > &result) const
{
@ -4686,56 +4448,6 @@ AbstractExternalFunctionNode::eval(const eval_context_t &eval_context) const thr
throw EvalExternalFunctionException();
}
pair<int, expr_t>
AbstractExternalFunctionNode::normalizeEquation(int var_endo, vector<pair<int, pair<expr_t, expr_t> > > &List_of_Op_RHS) const
{
vector<pair<bool, expr_t> > V_arguments;
vector<expr_t> V_expr_t;
bool present = false;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
V_arguments.push_back((*it)->normalizeEquation(var_endo, List_of_Op_RHS));
present = present || V_arguments[V_arguments.size()-1].first;
V_expr_t.push_back(V_arguments[V_arguments.size()-1].second);
}
if (!present)
return (make_pair(0, datatree.AddExternalFunction(symb_id, V_expr_t)));
else
return (make_pair(1, (expr_t) NULL));
}
expr_t
ExternalFunctionNode::toStatic(DataTree &static_datatree) const
{
vector<expr_t> static_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
static_arguments.push_back((*it)->toStatic(static_datatree));
return static_datatree.AddExternalFunction(symb_id, static_arguments);
}
expr_t
ExternalFunctionNode::cloneDynamic(DataTree &dynamic_datatree) const
{
vector<expr_t> dynamic_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
dynamic_arguments.push_back((*it)->cloneDynamic(dynamic_datatree));
return dynamic_datatree.AddExternalFunction(symb_id, dynamic_arguments);
}
expr_t
ExternalFunctionNode::cloneDynamicReindex(DataTree &dynamic_datatree, SymbolTable &orig_symbol_table) const
{
vector<expr_t> dynamic_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
dynamic_arguments.push_back((*it)->cloneDynamicReindex(dynamic_datatree, orig_symbol_table));
return dynamic_datatree.AddExternalFunction(dynamic_datatree.symbol_table.getID(orig_symbol_table.getName(symb_id)),
dynamic_arguments);
}
int
AbstractExternalFunctionNode::maxEndoLead() const
{
@ -4858,12 +4570,6 @@ AbstractExternalFunctionNode::differentiateForwardVars(const vector<string> &sub
return buildSimilarExternalFunctionNode(arguments_subst, datatree);
}
expr_t
ExternalFunctionNode::buildSimilarExternalFunctionNode(vector<expr_t> &alt_args, DataTree &alt_datatree) const
{
return alt_datatree.AddExternalFunction(symb_id, alt_args);
}
bool
AbstractExternalFunctionNode::alreadyWrittenAsTefTerm(int the_symb_id, deriv_node_temp_terms_t &tef_terms) const
{
@ -4936,10 +4642,342 @@ AbstractExternalFunctionNode::isInStaticForm() const
for (vector<expr_t>::const_iterator it = arguments.begin(); it != arguments.end(); ++it)
if (!(*it)->isInStaticForm())
return false;
return true;
}
pair<int, expr_t>
AbstractExternalFunctionNode::normalizeEquation(int var_endo, vector<pair<int, pair<expr_t, expr_t> > > &List_of_Op_RHS) const
{
vector<pair<bool, expr_t> > V_arguments;
vector<expr_t> V_expr_t;
bool present = false;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
V_arguments.push_back((*it)->normalizeEquation(var_endo, List_of_Op_RHS));
present = present || V_arguments[V_arguments.size()-1].first;
V_expr_t.push_back(V_arguments[V_arguments.size()-1].second);
}
if (!present)
return (make_pair(0, datatree.AddExternalFunction(symb_id, V_expr_t)));
else
return (make_pair(1, (expr_t) NULL));
}
void
AbstractExternalFunctionNode::writeExternalFunctionArguments(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
if (it != arguments.begin())
output << ",";
(*it)->writeOutput(output, output_type, temporary_terms, tef_terms);
}
}
void
AbstractExternalFunctionNode::writePrhs(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms, const string &ending) const
{
output << "mxArray *prhs"<< ending << "[nrhs"<< ending << "];" << endl;
int i = 0;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
{
output << "prhs" << ending << "[" << i++ << "] = mxCreateDoubleScalar("; // All external_function arguments are scalars
(*it)->writeOutput(output, output_type, temporary_terms, tef_terms);
output << ");" << endl;
}
}
bool
AbstractExternalFunctionNode::containsExternalFunction() const
{
return true;
}
ExternalFunctionNode::ExternalFunctionNode(DataTree &datatree_arg,
int symb_id_arg,
const vector<expr_t> &arguments_arg) :
AbstractExternalFunctionNode(datatree_arg, symb_id_arg, arguments_arg)
{
// Add myself to the external function map
datatree.external_function_node_map[make_pair(arguments, symb_id)] = this;
}
expr_t
ExternalFunctionNode::composeDerivatives(const vector<expr_t> &dargs)
{
vector<expr_t> dNodes;
for (int i = 0; i < (int) dargs.size(); i++)
dNodes.push_back(datatree.AddTimes(dargs.at(i),
datatree.AddFirstDerivExternalFunction(symb_id, arguments, i+1)));
expr_t theDeriv = datatree.Zero;
for (vector<expr_t>::const_iterator it = dNodes.begin(); it != dNodes.end(); it++)
theDeriv = datatree.AddPlus(theDeriv, *it);
return theDeriv;
}
void
ExternalFunctionNode::computeTemporaryTerms(map<expr_t, int> &reference_count,
temporary_terms_t &temporary_terms,
bool is_matlab) const
{
temporary_terms.insert(const_cast<ExternalFunctionNode *>(this));
}
void
ExternalFunctionNode::computeTemporaryTerms(map<expr_t, int> &reference_count,
temporary_terms_t &temporary_terms,
map<expr_t, pair<int, int> > &first_occurence,
int Curr_block,
vector< vector<temporary_terms_t> > &v_temporary_terms,
int equation) const
{
expr_t this2 = const_cast<ExternalFunctionNode *>(this);
temporary_terms.insert(this2);
first_occurence[this2] = make_pair(Curr_block, equation);
v_temporary_terms[Curr_block][equation].insert(this2);
}
void
ExternalFunctionNode::compile(ostream &CompileCode, unsigned int &instruction_number,
bool lhs_rhs, const temporary_terms_t &temporary_terms,
const map_idx_t &map_idx, bool dynamic, bool steady_dynamic,
deriv_node_temp_terms_t &tef_terms) const
{
temporary_terms_t::const_iterator it = temporary_terms.find(const_cast<ExternalFunctionNode *>(this));
if (it != temporary_terms.end())
{
if (dynamic)
{
map_idx_t::const_iterator ii = map_idx.find(idx);
FLDT_ fldt(ii->second);
fldt.write(CompileCode, instruction_number);
}
else
{
map_idx_t::const_iterator ii = map_idx.find(idx);
FLDST_ fldst(ii->second);
fldst.write(CompileCode, instruction_number);
}
return;
}
if (!lhs_rhs)
{
FLDTEF_ fldtef(getIndxInTefTerms(symb_id, tef_terms));
fldtef.write(CompileCode, instruction_number);
}
else
{
FSTPTEF_ fstptef(getIndxInTefTerms(symb_id, tef_terms));
fstptef.write(CompileCode, instruction_number);
}
}
void
ExternalFunctionNode::compileExternalFunctionOutput(ostream &CompileCode, unsigned int &instruction_number,
bool lhs_rhs, const temporary_terms_t &temporary_terms,
const map_idx_t &map_idx, bool dynamic, bool steady_dynamic,
deriv_node_temp_terms_t &tef_terms) const
{
int first_deriv_symb_id = datatree.external_functions_table.getFirstDerivSymbID(symb_id);
assert(first_deriv_symb_id != eExtFunSetButNoNameProvided);
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
(*it)->compileExternalFunctionOutput(CompileCode, instruction_number, lhs_rhs, temporary_terms,
map_idx, dynamic, steady_dynamic, tef_terms);
if (!alreadyWrittenAsTefTerm(symb_id, tef_terms))
{
tef_terms[make_pair(symb_id, arguments)] = (int) tef_terms.size();
int indx = getIndxInTefTerms(symb_id, tef_terms);
int second_deriv_symb_id = datatree.external_functions_table.getSecondDerivSymbID(symb_id);
assert(second_deriv_symb_id != eExtFunSetButNoNameProvided);
unsigned int nb_output_arguments = 0;
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
nb_output_arguments = 3;
else if (symb_id == first_deriv_symb_id)
nb_output_arguments = 2;
else
nb_output_arguments = 1;
unsigned int nb_input_arguments = compileExternalFunctionArguments(CompileCode, instruction_number, lhs_rhs, temporary_terms,
map_idx, dynamic, steady_dynamic, tef_terms);
FCALL_ fcall(nb_output_arguments, nb_input_arguments, datatree.symbol_table.getName(symb_id), indx);
switch (nb_output_arguments)
{
case 1:
fcall.set_function_type(ExternalFunctionWithoutDerivative);
break;
case 2:
fcall.set_function_type(ExternalFunctionWithFirstDerivative);
break;
case 3:
fcall.set_function_type(ExternalFunctionWithFirstandSecondDerivative);
break;
}
fcall.write(CompileCode, instruction_number);
FSTPTEF_ fstptef(indx);
fstptef.write(CompileCode, instruction_number);
}
}
void
ExternalFunctionNode::writeOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
if (output_type == oMatlabOutsideModel || output_type == oSteadyStateFile
|| output_type == oCSteadyStateFile || IS_LATEX(output_type))
{
string name = IS_LATEX(output_type) ? datatree.symbol_table.getTeXName(symb_id)
: datatree.symbol_table.getName(symb_id);
output << name << "(";
writeExternalFunctionArguments(output, output_type, temporary_terms, tef_terms);
output << ")";
return;
}
temporary_terms_t::const_iterator it = temporary_terms.find(const_cast<ExternalFunctionNode *>(this));
if (it != temporary_terms.end())
{
if (output_type == oMatlabDynamicModelSparse)
output << "T" << idx << "(it_)";
else
output << "T" << idx;
return;
}
if (IS_C(output_type))
output << "*";
output << "TEF_" << getIndxInTefTerms(symb_id, tef_terms);
}
void
ExternalFunctionNode::writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const
{
int first_deriv_symb_id = datatree.external_functions_table.getFirstDerivSymbID(symb_id);
assert(first_deriv_symb_id != eExtFunSetButNoNameProvided);
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
(*it)->writeExternalFunctionOutput(output, output_type, temporary_terms, tef_terms);
if (!alreadyWrittenAsTefTerm(symb_id, tef_terms))
{
tef_terms[make_pair(symb_id, arguments)] = (int) tef_terms.size();
int indx = getIndxInTefTerms(symb_id, tef_terms);
int second_deriv_symb_id = datatree.external_functions_table.getSecondDerivSymbID(symb_id);
assert(second_deriv_symb_id != eExtFunSetButNoNameProvided);
if (IS_C(output_type))
{
stringstream ending;
ending << "_tef_" << getIndxInTefTerms(symb_id, tef_terms);
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "int nlhs" << ending.str() << " = 3;" << endl
<< "double *TEF_" << indx << ", "
<< "*TEFD_" << indx << ", "
<< "*TEFDD_" << indx << ";" << endl;
else if (symb_id == first_deriv_symb_id)
output << "int nlhs" << ending.str() << " = 2;" << endl
<< "double *TEF_" << indx << ", "
<< "*TEFD_" << indx << "; " << endl;
else
output << "int nlhs" << ending.str() << " = 1;" << endl
<< "double *TEF_" << indx << ";" << endl;
output << "mxArray *plhs" << ending.str()<< "[nlhs"<< ending.str() << "];" << endl;
output << "int nrhs" << ending.str()<< " = " << arguments.size() << ";" << endl;
writePrhs(output, output_type, temporary_terms, tef_terms, ending.str());
output << "mexCallMATLAB("
<< "nlhs" << ending.str() << ", "
<< "plhs" << ending.str() << ", "
<< "nrhs" << ending.str() << ", "
<< "prhs" << ending.str() << ", \""
<< datatree.symbol_table.getName(symb_id) << "\");" << endl;
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl
<< "TEFD_" << indx << " = mxGetPr(plhs" << ending.str() << "[1]);" << endl
<< "TEFDD_" << indx << " = mxGetPr(plhs" << ending.str() << "[2]);" << endl
<< "int TEFDD_" << indx << "_nrows = (int)mxGetM(plhs" << ending.str()<< "[2]);" << endl;
else if (symb_id == first_deriv_symb_id)
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl
<< "TEFD_" << indx << " = mxGetPr(plhs" << ending.str() << "[1]);" << endl;
else
output << "TEF_" << indx << " = mxGetPr(plhs" << ending.str() << "[0]);" << endl;
}
else
{
if (symb_id == first_deriv_symb_id
&& symb_id == second_deriv_symb_id)
output << "[TEF_" << indx << " TEFD_"<< indx << " TEFDD_"<< indx << "] = ";
else if (symb_id == first_deriv_symb_id)
output << "[TEF_" << indx << " TEFD_"<< indx << "] = ";
else
output << "TEF_" << indx << " = ";
output << datatree.symbol_table.getName(symb_id) << "(";
writeExternalFunctionArguments(output, output_type, temporary_terms, tef_terms);
output << ");" << endl;
}
}
}
expr_t
ExternalFunctionNode::toStatic(DataTree &static_datatree) const
{
vector<expr_t> static_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
static_arguments.push_back((*it)->toStatic(static_datatree));
return static_datatree.AddExternalFunction(symb_id, static_arguments);
}
expr_t
ExternalFunctionNode::cloneDynamic(DataTree &dynamic_datatree) const
{
vector<expr_t> dynamic_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
dynamic_arguments.push_back((*it)->cloneDynamic(dynamic_datatree));
return dynamic_datatree.AddExternalFunction(symb_id, dynamic_arguments);
}
expr_t
ExternalFunctionNode::cloneDynamicReindex(DataTree &dynamic_datatree, SymbolTable &orig_symbol_table) const
{
vector<expr_t> dynamic_arguments;
for (vector<expr_t>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
dynamic_arguments.push_back((*it)->cloneDynamicReindex(dynamic_datatree, orig_symbol_table));
return dynamic_datatree.AddExternalFunction(dynamic_datatree.symbol_table.getID(orig_symbol_table.getName(symb_id)),
dynamic_arguments);
}
expr_t
ExternalFunctionNode::buildSimilarExternalFunctionNode(vector<expr_t> &alt_args, DataTree &alt_datatree) const
{
return alt_datatree.AddExternalFunction(symb_id, alt_args);
}
FirstDerivExternalFunctionNode::FirstDerivExternalFunctionNode(DataTree &datatree_arg,
int top_level_symb_id_arg,
const vector<expr_t> &arguments_arg,

9
preprocessor/ExprNode.hh
View File

@ -187,6 +187,9 @@ public:
*/
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const = 0;
//! returns true if the expr node contains an external function
virtual bool containsExternalFunction() const = 0;
//! Writes output of node (with no temporary terms and with "outside model" output type)
void writeOutput(ostream &output) const;
@ -444,6 +447,7 @@ public:
};
virtual void prepareForDerivation();
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const;
virtual bool containsExternalFunction() const;
virtual void collectDynamicVariables(SymbolType type_arg, set<pair<int, int> > &result) const;
virtual void collectTemporary_terms(const temporary_terms_t &temporary_terms, temporary_terms_inuse_t &temporary_terms_inuse, int Curr_Block) const;
virtual double eval(const eval_context_t &eval_context) const throw (EvalException, EvalExternalFunctionException);
@ -490,6 +494,7 @@ public:
VariableNode(DataTree &datatree_arg, int symb_id_arg, int lag_arg);
virtual void prepareForDerivation();
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const;
virtual bool containsExternalFunction() const;
virtual void collectDynamicVariables(SymbolType type_arg, set<pair<int, int> > &result) const;
virtual void computeTemporaryTerms(map<expr_t, int> &reference_count,
temporary_terms_t &temporary_terms,
@ -557,6 +562,7 @@ public:
virtual void prepareForDerivation();
virtual void computeTemporaryTerms(map<expr_t, int> &reference_count, temporary_terms_t &temporary_terms, bool is_matlab) const;
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const;
virtual bool containsExternalFunction() const;
virtual void writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const;
@ -636,6 +642,7 @@ public:
virtual int precedence(ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms) const;
virtual void computeTemporaryTerms(map<expr_t, int> &reference_count, temporary_terms_t &temporary_terms, bool is_matlab) const;
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const;
virtual bool containsExternalFunction() const;
virtual void writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const;
@ -731,6 +738,7 @@ public:
virtual int precedence(ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms) const;
virtual void computeTemporaryTerms(map<expr_t, int> &reference_count, temporary_terms_t &temporary_terms, bool is_matlab) const;
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const;
virtual bool containsExternalFunction() const;
virtual void writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const;
@ -803,6 +811,7 @@ public:
virtual void prepareForDerivation();
virtual void computeTemporaryTerms(map<expr_t, int> &reference_count, temporary_terms_t &temporary_terms, bool is_matlab) const = 0;
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_t &temporary_terms, deriv_node_temp_terms_t &tef_terms) const = 0;
virtual bool containsExternalFunction() const;
virtual void writeExternalFunctionOutput(ostream &output, ExprNodeOutputType output_type,
const temporary_terms_t &temporary_terms,
deriv_node_temp_terms_t &tef_terms) const = 0;

200
preprocessor/ModFile.cc
View File

@ -490,55 +490,55 @@ ModFile::computingPass(bool no_tmp_terms, FileOutputType output)
// Compute static model and its derivatives
dynamic_model.toStatic(static_model);
if (!no_static)
{
if (mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
static_model.set_cutoff_to_zero();
{
if (mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
static_model.set_cutoff_to_zero();
const bool static_hessian = mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation;
const bool paramsDerivatives = mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation;
static_model.computingPass(global_eval_context, no_tmp_terms, static_hessian,
false, paramsDerivatives, block, byte_code);
}
const bool static_hessian = mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation;
const bool paramsDerivatives = mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation;
static_model.computingPass(global_eval_context, no_tmp_terms, static_hessian,
false, paramsDerivatives, block, byte_code);
}
// Set things to compute for dynamic model
if (mod_file_struct.perfect_foresight_solver_present || mod_file_struct.check_present
|| mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
{
if (mod_file_struct.perfect_foresight_solver_present)
dynamic_model.computingPass(true, false, false, false, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
else
{
if (mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
dynamic_model.set_cutoff_to_zero();
if (mod_file_struct.order_option < 1 || mod_file_struct.order_option > 3)
{
cerr << "ERROR: Incorrect order option..." << endl;
exit(EXIT_FAILURE);
}
bool hessian = mod_file_struct.order_option >= 2
|| mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation
|| output == second
|| output == third;
bool thirdDerivatives = mod_file_struct.order_option == 3
|| mod_file_struct.estimation_analytic_derivation
|| output == third;
bool paramsDerivatives = mod_file_struct.identification_present || mod_file_struct.estimation_analytic_derivation;
dynamic_model.computingPass(true, hessian, thirdDerivatives, paramsDerivatives, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
}
}
else // No computing task requested, compute derivatives up to 2nd order by default
dynamic_model.computingPass(true, true, false, false, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
|| mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
{
if (mod_file_struct.perfect_foresight_solver_present)
dynamic_model.computingPass(true, false, false, false, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
else
{
if (mod_file_struct.stoch_simul_present
|| mod_file_struct.estimation_present || mod_file_struct.osr_present
|| mod_file_struct.ramsey_model_present || mod_file_struct.identification_present
|| mod_file_struct.calib_smoother_present)
dynamic_model.set_cutoff_to_zero();
if (mod_file_struct.order_option < 1 || mod_file_struct.order_option > 3)
{
cerr << "ERROR: Incorrect order option..." << endl;
exit(EXIT_FAILURE);
}
bool hessian = mod_file_struct.order_option >= 2
|| mod_file_struct.identification_present
|| mod_file_struct.estimation_analytic_derivation
|| output == second
|| output == third;
bool thirdDerivatives = mod_file_struct.order_option == 3
|| mod_file_struct.estimation_analytic_derivation
|| output == third;
bool paramsDerivatives = mod_file_struct.identification_present || mod_file_struct.estimation_analytic_derivation;
dynamic_model.computingPass(true, hessian, thirdDerivatives, paramsDerivatives, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
}
}
else // No computing task requested, compute derivatives up to 2nd order by default
dynamic_model.computingPass(true, true, false, false, global_eval_context, no_tmp_terms, block, use_dll, byte_code);
}
for (vector<Statement *>::iterator it = statements.begin();
@ -547,7 +547,8 @@ ModFile::computingPass(bool no_tmp_terms, FileOutputType output)
}
void
ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_global, bool no_log, bool no_warn, bool console, bool nograph, bool nointeractive, const ConfigFile &config_file
ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_global, bool no_log, bool no_warn, bool console, bool nograph,
bool nointeractive, const ConfigFile &config_file, bool check_model_changes
#if defined(_WIN32) || defined(__CYGWIN32__)
, bool cygwin, bool msvc
#endif
@ -595,6 +596,9 @@ ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_glo
<< "global M_ oo_ options_ ys0_ ex0_ estimation_info" << endl
<< "options_ = [];" << endl
<< "M_.fname = '" << basename << "';" << endl
<< "M_.dynare_version = '" << PACKAGE_VERSION << "';" << endl
<< "oo_.dynare_version = '" << PACKAGE_VERSION << "';" << endl
<< "options_.dynare_version = '" << PACKAGE_VERSION << "';" << endl
<< "%" << endl
<< "% Some global variables initialization" << endl
<< "%" << endl;
@ -613,7 +617,7 @@ ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_glo
if (nointeractive)
mOutputFile << "options_.nointeractive = 1;" << endl;
cout << "Processing outputs ...";
cout << "Processing outputs ..." << endl;
symbol_table.writeOutput(mOutputFile);
@ -667,18 +671,25 @@ ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_glo
<< " error('DYNARE: Can''t find bytecode DLL. Please compile it or remove the ''bytecode'' option.')" << endl
<< "end" << endl;
// Erase possible remnants of previous runs
unlink((basename + "_dynamic.m").c_str());
unlink((basename + "_dynamic.cod").c_str());
unlink((basename + "_dynamic.bin").c_str());
bool hasModelChanged = !dynamic_model.isChecksumMatching(basename);
if (!check_model_changes)
hasModelChanged = true;
if (hasModelChanged)
{
// Erase possible remnants of previous runs
unlink((basename + "_dynamic.m").c_str());
unlink((basename + "_dynamic.cod").c_str());
unlink((basename + "_dynamic.bin").c_str());
unlink((basename + "_static.m").c_str());
unlink((basename + "_static.cod").c_str());
unlink((basename + "_static.bin").c_str());
unlink((basename + "_steadystate2.m").c_str());
unlink((basename + "_set_auxiliary_variables.m").c_str());
unlink((basename + "_static.m").c_str());
unlink((basename + "_static.cod").c_str());
unlink((basename + "_static.bin").c_str());
unlink((basename + "_steadystate2.m").c_str());
unlink((basename + "_set_auxiliary_variables.m").c_str());
}
if (!use_dll)
{
mOutputFile << "erase_compiled_function('" + basename + "_static');" << endl;
@ -713,55 +724,22 @@ ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_glo
#endif
// Compile the dynamic MEX file for use_dll option
// When check_model_changes is true, don't force compile if MEX is fresher than source
if (use_dll)
{
mOutputFile << "if ~exist('OCTAVE_VERSION')" << endl;
// Some mex commands are enclosed in an eval(), because otherwise it will make Octave fail
#if defined(_WIN32) || defined(__CYGWIN32__)
if (msvc)
// MATLAB/Windows + Microsoft Visual C++
mOutputFile << " eval('mex -O LINKFLAGS=\"$LINKFLAGS /export:Dynamic\" " << basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LINKFLAGS=\"$LINKFLAGS /export:Static\" " << basename << "_static.c "<< basename << "_static_mex.c')" << endl;
mOutputFile << "dyn_mex('msvc', '" << basename << "', " << !check_model_changes << ")" << endl;
else if (cygwin)
// MATLAB/Windows + Cygwin g++
mOutputFile << " eval('mex -O PRELINK_CMDS1=\"echo EXPORTS > mex.def & echo mexFunction >> mex.def & echo Dynamic >> mex.def\" " << basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O PRELINK_CMDS1=\"echo EXPORTS > mex.def & echo mexFunction >> mex.def & echo Static >> mex.def\" " << basename << "_static.c "<< basename << "_static_mex.c')" << endl;
mOutputFile << "dyn_mex('cygwin', '" << basename << "', " << !check_model_changes << ")" << endl;
else
mOutputFile << " error('When using the USE_DLL option, you must give either ''cygwin'' or ''msvc'' option to the ''dynare'' command')" << endl;
#else
# ifdef __linux__
// MATLAB/Linux
mOutputFile << " if matlab_ver_less_than('8.3')" << endl
<< " eval('mex -O LDFLAGS=''-pthread -shared -Wl,--no-undefined'' " << basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LDFLAGS=''-pthread -shared -Wl,--no-undefined'' " << basename << "_static.c "<< basename << "_static_mex.c')" << endl
<< " else" << endl
<< " eval('mex -O LINKEXPORT='''' " << basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LINKEXPORT='''' " << basename << "_static.c "<< basename << "_static_mex.c')" << endl
<< " end" << endl;
# else // MacOS
// MATLAB/MacOS
mOutputFile << " if matlab_ver_less_than('8.3')" << endl
<< " if matlab_ver_less_than('8.1')" << endl
<< " eval('mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined error -arch \\$ARCHS -Wl,-syslibroot,\\$SDKROOT -mmacosx-version-min=\\$MACOSX_DEPLOYMENT_TARGET -bundle'' "
<< basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined error -arch \\$ARCHS -Wl,-syslibroot,\\$SDKROOT -mmacosx-version-min=\\$MACOSX_DEPLOYMENT_TARGET -bundle'' "
<< basename << "_static.c " << basename << "_static_mex.c')" << endl
<< " else" << endl
<< " eval('mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined error -arch \\$ARCHS -Wl,-syslibroot,\\$MW_SDKROOT -mmacosx-version-min=\\$MACOSX_DEPLOYMENT_TARGET -bundle'' "
<< basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LDFLAGS=''-Wl,-twolevel_namespace -undefined error -arch \\$ARCHS -Wl,-syslibroot,\\$MW_SDKROOT -mmacosx-version-min=\\$MACOSX_DEPLOYMENT_TARGET -bundle'' "
<< basename << "_static.c " << basename << "_static_mex.c')" << endl
<< " end" << endl
<< " else" << endl
<< " eval('mex -O LINKEXPORT='''' " << basename << "_dynamic.c " << basename << "_dynamic_mex.c')" << endl
<< " eval('mex -O LINKEXPORT='''' " << basename << "_static.c "<< basename << "_static_mex.c')" << endl
<< " end" << endl;
# endif
// other configurations
mOutputFile << "dyn_mex('', '" << basename << "', " << !check_model_changes << ")" << endl;
#endif
mOutputFile << "else" << endl // Octave
<< " mex " << basename << "_dynamic.c " << basename << "_dynamic_mex.c" << endl
<< " mex " << basename << "_static.c " << basename << "_static_mex.c" << endl
<< "end" << endl;
}
// Add path for block option with M-files
@ -842,22 +820,24 @@ ModFile::writeOutputFiles(const string &basename, bool clear_all, bool clear_glo
mOutputFile.close();
// Create static and dynamic files
if (dynamic_model.equation_number() > 0)
if (hasModelChanged)
{
if (!no_static)
{
static_model.writeStaticFile(basename, block, byte_code, use_dll);
static_model.writeParamsDerivativesFile(basename);
}
// Create static and dynamic files
if (dynamic_model.equation_number() > 0)
{
if (!no_static)
{
static_model.writeStaticFile(basename, block, byte_code, use_dll);
static_model.writeParamsDerivativesFile(basename);
}
dynamic_model.writeDynamicFile(basename, block, byte_code, use_dll, mod_file_struct.order_option);
dynamic_model.writeParamsDerivativesFile(basename);
dynamic_model.writeDynamicFile(basename, block, byte_code, use_dll, mod_file_struct.order_option);
dynamic_model.writeParamsDerivativesFile(basename);
}
// Create steady state file
steady_state_model.writeSteadyStateFile(basename, mod_file_struct.ramsey_model_present);
}
// Create steady state file
steady_state_model.writeSteadyStateFile(basename, mod_file_struct.ramsey_model_present);
cout << "done" << endl;
}

12
preprocessor/ModFile.hh
View File

@ -24,6 +24,8 @@ using namespace std;
#include <ostream>
#include <ctime>
#include <iostream>
#include <sstream>
#include "SymbolTable.hh"
#include "NumericalConstants.hh"
@ -37,6 +39,11 @@ using namespace std;
#include "WarningConsolidation.hh"
#include "ExtendedPreprocessorTypes.hh"
// for checksum computation
#ifndef PRIVATE_BUFFER_SIZE
#define PRIVATE_BUFFER_SIZE 1024
#endif
//! The abstract representation of a "mod" file
class ModFile
{
@ -138,7 +145,8 @@ public:
\param cygwin Should the MEX command of use_dll be adapted for Cygwin?
\param msvc Should the MEX command of use_dll be adapted for MSVC?
*/
void writeOutputFiles(const string &basename, bool clear_all, bool clear_global, bool no_log, bool no_warn, bool console, bool nograph, bool nointeractive, const ConfigFile &config_file
void writeOutputFiles(const string &basename, bool clear_all, bool clear_global, bool no_log, bool no_warn,
bool console, bool nograph, bool nointeractive, const ConfigFile &config_file, bool check_model_changes
#if defined(_WIN32) || defined(__CYGWIN32__)
, bool cygwin, bool msvc
#endif
@ -153,6 +161,8 @@ public:
//! Writes Cpp output files only => No further Matlab processing
void writeCCOutputFiles(const string &basename) const;
void writeModelCC(const string &basename) const;
void computeChecksum();
};
#endif // ! MOD_FILE_HH

28
preprocessor/ParsingDriver.cc
View File

@ -1630,6 +1630,34 @@ ParsingDriver::optim_options_num(string *name, string *value)
delete value;
}
void
ParsingDriver::tarb_optim_options_helper(const string &name)
{
if (options_list.string_options.find("TaRB.optim_opt") == options_list.string_options.end())
options_list.string_options["TaRB.optim_opt"] = "";
else
options_list.string_options["TaRB.optim_opt"] += ",";
options_list.string_options["TaRB.optim_opt"] += "''" + name + "'',";
}
void
ParsingDriver::tarb_optim_options_string(string *name, string *value)
{
tarb_optim_options_helper(*name);
options_list.string_options["TaRB.optim_opt"] += "''" + *value + "''";
delete name;
delete value;
}
void
ParsingDriver::tarb_optim_options_num(string *name, string *value)
{
tarb_optim_options_helper(*name);
options_list.string_options["TaRB.optim_opt"] += *value;
delete name;
delete value;
}
void
ParsingDriver::check_varobs()
{

5
preprocessor/ParsingDriver.hh
View File

@ -98,6 +98,7 @@ private:
//! Creates option "optim_opt" in OptionsList if it doesn't exist, else add a comma, and adds the option name
void optim_options_helper(const string &name);
void tarb_optim_options_helper(const string &name);
//! Stores temporary symbol table
SymbolList symbol_list;
@ -443,6 +444,10 @@ public:
void optim_options_string(string *name, string *value);
//! Adds an optimization option (numeric value)
void optim_options_num(string *name, string *value);
//! Adds a TaRB optimization option (string value)
void tarb_optim_options_string(string *name, string *value);
//! Adds a TaRB optimization option (numeric value)
void tarb_optim_options_num(string *name, string *value);
//! Check that no observed variable has yet be defined
void check_varobs();
//! Add a new observed variable

11
preprocessor/StaticModel.cc
View File

@ -1,5 +1,5 @@
/*
* Copyright (C) 2003-2014 Dynare Team
* Copyright (C) 2003-2015 Dynare Team
*
* This file is part of Dynare.
*
@ -1927,9 +1927,16 @@ void StaticModel::writeAuxVarRecursiveDefinitions(const string &basename) const
<< "% from model file (.mod)" << endl
<< endl;
deriv_node_temp_terms_t tef_terms;
temporary_terms_t temporary_terms;
for (int i = 0; i < (int) aux_equations.size(); i++)
if (dynamic_cast<ExprNode *>(aux_equations[i])->containsExternalFunction())
dynamic_cast<ExprNode *>(aux_equations[i])->writeExternalFunctionOutput(output, oMatlabStaticModel,
temporary_terms, tef_terms);
for (int i = 0; i < (int) aux_equations.size(); i++)
{
dynamic_cast<ExprNode *>(aux_equations[i])->writeOutput(output, oMatlabStaticModel);
dynamic_cast<ExprNode *>(aux_equations[i])->writeOutput(output, oMatlabStaticModel, temporary_terms, tef_terms);
output << ";" << endl;
}
}

76
tests/Makefile.am
View File

@ -6,7 +6,10 @@ MODFILES = \
estimation/fs2000_MCMC_jumping_covariance.mod \
estimation/fs2000_initialize_from_calib.mod \
estimation/fs2000_calibrated_covariance.mod \
estimation/MH_recover/fs2000_recover.mod \
estimation/t_proposal/fs2000_student.mod \
gsa/ls2003.mod \
gsa/ls2003a.mod \
ramst.mod \
ramst_a.mod \
ramst_static_tag.mod \
@ -89,15 +92,11 @@ MODFILES = \
fs2000/fs2000.mod \
fs2000/fs2000a.mod \
fs2000/fs2000c.mod \
fs2000/fs2000d.mod \
fs2000/fs2000e.mod \
fs2000/fs2000_cmaes.mod \
fs2000/fs2000_calib.mod \
fs2000/fs2000_calib_dseries.mod \
fs2000/fs2000_analytic_derivation.mod \
fs2000/fs2000_missing_data.mod \
fs2000/fs2000_sd.mod \
fs2000/fs2000_mode_compute_8.mod \
fs2000/fs2000_dseries_a.mod \
fs2000/fs2000_dseries_b.mod \
homotopy/homotopy1_test.mod \
@ -136,6 +135,7 @@ MODFILES = \
conditional_forecasts/fs2000_est.mod \
recursive/ls2003.mod \
recursive/ls2003_bayesian.mod \
recursive/ls2003_bayesian_xls.mod \
ms-sbvar/test_exclusions.mod \
ms-sbvar/test_exclusions_nc.mod \
ms-sbvar/test_lower_cholesky.mod \
@ -183,6 +183,7 @@ MODFILES = \
deterministic_simulations/rbc_det_exo_lag_2a.mod \
deterministic_simulations/rbc_det_exo_lag_2b.mod \
deterministic_simulations/rbc_det_exo_lag_2c.mod \
deterministic_simulations/sim_several_leads_lags.mod \
walsh.mod \
measurement_errors/fs2000_corr_me_ml_mcmc/fs2000_corr_ME.mod \
trend_var/fs2000_nonstationary.mod \
@ -205,6 +206,21 @@ MODFILES = \
smoother2histval/fs2000_simul.mod \
smoother2histval/fs2000_smooth.mod \
smoother2histval/fs2000_smooth_stoch_simul.mod \
optimizers/fs2000_2.mod \
optimizers/fs2000_3.mod \
optimizers/fs2000_4.mod \
optimizers/fs2000_4_with_optim.mod \
optimizers/fs2000_5.mod \
optimizers/fs2000_6.mod \
optimizers/fs2000_7.mod \
optimizers/fs2000_8.mod \
optimizers/fs2000_8_with_optim.mod \
optimizers/fs2000_9.mod \
optimizers/fs2000_10.mod \
optimizers/fs2000_101.mod \
optimizers/fs2000_102.mod \
optimizers/fs2000_w.mod \
differentiate_forward_vars/RBC_differentiate_forward.mod \
reporting/example1.mod
XFAIL_MODFILES = ramst_xfail.mod \
@ -394,7 +410,9 @@ EXTRA_DIST = \
loglinear/results_exp.mat \
smoother2histval/fsdat_simul.m \
optimal_policy/Ramsey/find_c.m \
optimal_policy/Ramsey/oo_ramsey_policy_initval.mat
optimal_policy/Ramsey/oo_ramsey_policy_initval.mat \
optimizers/optimizer_function_wrapper.m \
optimizers/fs2000.common.inc
TARGETS =
@ -419,30 +437,56 @@ check-octave: $(O_XFAIL_TRS_FILES) $(O_TRS_FILES)
@echo 'Octave Tests Done'
%.m.trs %.m.log: %.mod
DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -logfile $*.m.log -r run_test_matlab
@echo "`tput bold``tput setaf 3`MATLAB: $(PWD)/$*... `tput sgr0`"
@DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -r run_test_matlab > $*.m.log 2> /dev/null
@if grep -q ":test-result: PASS" $*.m.trs; then \
echo "`tput bold``tput setaf 2`MATLAB: $(PWD)/$* PASSED!`tput sgr0`" ; \
else \
echo "`tput bold``tput setaf 1`MATLAB: $(PWD)/$* FAILED!`tput sgr0`" ; \
fi
%.m.drs %.m.log: %.m.trs
@cat $*.m.log
%.m.trs %.m.log : %.m
DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -logfile $*.m.log -r $*
@echo "`tput bold``tput setaf 3`MATLAB: $(PWD)/$*... `tput sgr0`"
@DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -r $* > $*.m.log 2> /dev/null
@echo "`tput bold``tput setaf 3`MATLAB: $(PWD)/$* Done!`tput sgr0`"
%.o.trs %.o.log: %.mod
DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
@echo "`tput bold``tput setaf 3`OCTAVE: $(PWD)/$*... `tput sgr0`"
@DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(OCTAVE) --no-init-file --silent --no-history run_test_octave.m > $*.o.log 2>&1
@if grep -q ":test-result: PASS" $*.o.trs; then \
echo "`tput bold``tput setaf 2`OCTAVE: $(PWD)/$* PASSED!`tput sgr0`" ; \
else \
echo "`tput bold``tput setaf 1`OCTAVE: $(PWD)/$* FAILED!`tput sgr0`" ; \
fi
%.o.drs %.o.log: %.mod %.o.trs
@cat $*.o.log
%.o.trs %.o.log : %.m
DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" \
@echo "`tput bold``tput setaf 3`OCTAVE: $(PWD)/$*... `tput sgr0`"
@DYNARE_VERSION="$(PACKAGE_VERSION)" TOP_TEST_DIR="$(PWD)" \
$(OCTAVE) --no-init-file --silent --no-history $< > $*.o.log 2>&1
@echo "`tput bold``tput setaf 3`OCTAVE: $(PWD)/$* Done!`tput sgr0`"
%.m.tls : %.m
TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -r run_m_script
touch $*.m.tls
@echo "`tput bold``tput setaf 3`MATLAB: $(PWD)/$*... `tput sgr0`"
@TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(MATLAB)/bin/matlab -nosplash -nodisplay -r run_m_script 2> /dev/null
@touch $*.m.tls
@echo "`tput bold`MATLAB`tput setaf 3`: $(PWD)/$* Done!`tput sgr0`"
%.o.tls : %.m
TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
@echo "`tput bold``tput setaf 3`OCTAVE: $(PWD)/$*... `tput sgr0`"
@TOP_TEST_DIR="$(PWD)" FILESTEM="$*" \
$(OCTAVE) --no-init-file --silent --no-history run_o_script.m 2>&1
@touch $*.o.tls
@echo "`tput bold``tput setaf 3`OCTAVE: $(PWD)/$* Done!`tput sgr0`"
clean-local:
rm -f $(M_TRS_FILES) \

35
tests/deterministic_simulations/sim_several_leads_lags.mod Normal file
View File

@ -0,0 +1,35 @@
var c k z_forward z_backward;
varexo x z_shock;
parameters alph gam delt bet aa;
alph=0.5;
gam=0.5;
delt=0.02;
bet=0.05;
aa=0.5;
model;
c + k - aa*x*k(-1)^alph - (1-delt)*k(-1); // Resource constraint
c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam); // Euler equation
z_backward=0.1*1+0.3*z_backward(-1)+0.3*z_backward(-2)+0.3*z_backward(-3)+(x(-4)-1);
z_forward=0.1*1+0.45*z_forward(+1)+0.45*z_forward(+2)+(x(+4)-1);
end;
initval;
c = 1.2;
k = 12;
x = 1;
end;
histval;
x(-1)=1.30;
x(-2)=1.30;
end;
shocks;
var x;
periods 2;
values 0.9;
end;
simul(periods=200,maxit=100);

92
tests/differentiate_forward_vars/RBC_differentiate_forward.mod Normal file
View File

@ -0,0 +1,92 @@
/*
* This model shows how to use the differentiate_forward_vars option to simulate
* perfect foresight models when the steady state is unknown
* or when the model is very persistent. In this file, we consider an RBC model
* with a CES technology and very persistent productivity shock. We set the
* autoregressive parameter of this exogenous productivity to 0.999, so that
* in period 400 the level of productivity, after an initial one percent shock,
* is still 0.67\% above its steady state level.
*
* Written by Stéphane Adjemian. For more information, see
* http://gitlab.ithaca.fr/Dynare/differentiate-forward-variables
*/
var Capital, Output, Labour, Consumption, Efficiency, efficiency, ExpectedTerm;
varexo EfficiencyInnovation;
parameters beta, theta, tau, alpha, psi, delta, rho, effstar, sigma;
/*
** Calibration
*/
beta = 0.990;
theta = 0.357;
tau = 30.000;
alpha = 0.450;
psi = -1.000; // So that the elasticity of substitution between inputs is 1/(1-psi)=1/10
delta = 0.020;
rho = 0.999;
effstar = 1.000;
sigma = 0.010;
model(differentiate_forward_vars);
// Eq. n°1:
efficiency = rho*efficiency(-1) + sigma*EfficiencyInnovation;
// Eq. n°2:
Efficiency = effstar*exp(efficiency);
// Eq. n°3:
Output = Efficiency*(alpha*(Capital(-1)^psi)+(1-alpha)*(Labour^psi))^(1/psi);
// Eq. n°4:
Consumption + Capital - Output - (1-delta)*Capital(-1);
// Eq. n°5:
((1-theta)/theta)*(Consumption/(1-Labour)) - (1-alpha)*(Output/Labour)^(1-psi);
// Eq. n°6:
(((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption - ExpectedTerm(1);
// Eq. n°7:
ExpectedTerm = beta*((((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption)*(alpha*((Output/Capital(-1))^(1-psi))+1-delta);
end;
steady_state_model;
efficiency = 0;
Efficiency = effstar;
// Compute some steady state ratios.
Output_per_unit_of_Capital=((1/beta-1+delta)/alpha)^(1/(1-psi));
Consumption_per_unit_of_Capital=Output_per_unit_of_Capital-delta;
Labour_per_unit_of_Capital=(((Output_per_unit_of_Capital/Efficiency)^psi-alpha)/(1-alpha))^(1/psi);
Output_per_unit_of_Labour=Output_per_unit_of_Capital/Labour_per_unit_of_Capital;
Consumption_per_unit_of_Labour=Consumption_per_unit_of_Capital/Labour_per_unit_of_Capital;
// Compute steady state share of capital.
ShareOfCapital=alpha/(alpha+(1-alpha)*Labour_per_unit_of_Capital^psi);
// Compute steady state of the endogenous variables.
Labour=1/(1+Consumption_per_unit_of_Labour/((1-alpha)*theta/(1-theta)*Output_per_unit_of_Labour^(1-psi)));
Consumption = Consumption_per_unit_of_Labour*Labour;
Capital = Labour/Labour_per_unit_of_Capital;
Output = Output_per_unit_of_Capital*Capital;
ExpectedTerm = beta*((((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption)*(alpha*((Output/Capital)^(1-psi))+1-delta);
end;
shocks;
var EfficiencyInnovation;
periods 1;
values 1;
end;
steady;
check;
simul(periods=500);

139
tests/estimation/MH_recover/fs2000_recover.mod Normal file
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@ -0,0 +1,139 @@
/*
* This file replicates the estimation of the cash in advance model described
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
* Journal of Applied Econometrics, 15(6), 645-670.
*
* The data are in file "fsdat_simul.m", and have been artificially generated.
* They are therefore different from the original dataset used by Schorfheide.
*
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
* implications of long-run neutrality for monetary business cycle models",
* Journal of Applied Econometrics, 9, S37-S70.
* Note that there is an initial minus sign missing in equation (A1), p. S63.
*
* This implementation was written by Michel Juillard. Please note that the
* following copyright notice only applies to this Dynare implementation of the
* model.
*/
/*
* Copyright (C) 2004-2010 Dynare Team
*
* This file is part of Dynare.
*
* Dynare is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Dynare is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
*/
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady_state_model;
dA = exp(gam);
gst = 1/dA;
m = mst;
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
n = xist/(nust+xist);
P = xist + nust;
k = khst*n;
l = psi*mst*n/( (1-psi)*(1-n) );
c = mst/P;
d = l - mst + 1;
y = k^alp*n^(1-alp)*gst^alp;
R = mst/bet;
W = l/n;
ist = y-c;
q = 1 - d;
e = 1;
gp_obs = m/dA;
gy_obs = dA;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.MaxNumberOfBytes=2000*11*8/4;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8);
copyfile([M_.dname filesep 'metropolis' filesep 'fs2000_recover_mh1_blck1.mat'],'fs2000_mh1_blck1.mat')
copyfile([M_.dname filesep 'metropolis' filesep 'fs2000_recover_mh3_blck2.mat'],'fs2000_mh3_blck2.mat')
delete([M_.dname filesep 'metropolis' filesep 'fs2000_recover_mh4_blck2.mat'])
estimation(order=1, datafile='../fsdat_simul',mode_compute=0,mode_file=fs2000_recover_mode, nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8,mh_recover);
%check first unaffected chain
temp1=load('fs2000_mh1_blck1.mat');
temp2=load([M_.dname filesep 'Metropolis' filesep 'fs2000_recover_mh1_blck1.mat']);
if max(max(temp1.x2-temp2.x2))>1e-10
error('Draws of unaffected chain are not the same')
end
%check second, affected chain with last unaffected file
temp1=load('fs2000_mh3_blck2.mat');
temp2=load([M_.dname filesep 'Metropolis' filesep 'fs2000_recover_mh3_blck2.mat']);
if max(max(temp1.x2-temp2.x2))>1e-10
error('Draws of affected chain''s unaffected files are not the same')
end

120
tests/estimation/t_proposal/fs2000_student.mod Normal file
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@ -0,0 +1,120 @@
/*
* This file replicates the estimation of the cash in advance model described
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
* Journal of Applied Econometrics, 15(6), 645-670.
*
* The data are in file "fsdat_simul.m", and have been artificially generated.
* They are therefore different from the original dataset used by Schorfheide.
*
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
* implications of long-run neutrality for monetary business cycle models",
* Journal of Applied Econometrics, 9, S37-S70.
* Note that there is an initial minus sign missing in equation (A1), p. S63.
*
* This implementation was written by Michel Juillard. Please note that the
* following copyright notice only applies to this Dynare implementation of the
* model.
*/
/*
* Copyright (C) 2004-2010 Dynare Team
*
* This file is part of Dynare.
*
* Dynare is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Dynare is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
*/
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady_state_model;
dA = exp(gam);
gst = 1/dA;
m = mst;
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
n = xist/(nust+xist);
P = xist + nust;
k = khst*n;
l = psi*mst*n/( (1-psi)*(1-n) );
c = mst/P;
d = l - mst + 1;
y = k^alp*n^(1-alp)*gst^alp;
R = mst/bet;
W = l/n;
ist = y-c;
q = 1 - d;
e = 1;
gp_obs = m/dA;
gy_obs = dA;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.proposal_distribution='rand_multivariate_student';
options_.student_degrees_of_freedom=5;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=2002, mh_nblocks=2, mh_jscale=0.8,mode_compute=4);

75
tests/fs2000/fs2000_cmaes.mod
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@ -1,75 +0,0 @@
// See fs2000.mod in the examples/ directory for details on the model
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mode_compute=9,mh_replic=0);

82
tests/fs2000/fs2000_mode_compute_8.mod
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@ -1,82 +0,0 @@
// See fs2000.mod in the examples/ directory for details on the model
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,mode_compute=8,loglinear,mh_replic=0,optim=(
'MaxIter',5000,
'TolFun',1e-4,
'TolX',1e-4,
'MaxFunEvals',5000,
'MaxFunEvalFactor',500,
'InitialSimplexSize',0.05
));

75
tests/fs2000/fs2000d.mod
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@ -1,75 +0,0 @@
// See fs2000.mod in the examples/ directory for details on the model
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,mode_compute=5,loglinear,mh_replic=0);

75
tests/fs2000/fs2000e.mod
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@ -1,75 +0,0 @@
// See fs2000.mod in the examples/ directory for details on the model
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,optim=('NumgradEpsilon',1e-6,'NumgradAlgorithm',3,'MaxIter',100,'InitialInverseHessian','eye(9)*.0001'),loglinear,mh_replic=0);

32
tests/gsa/ls2003a.mod
View File

@ -54,11 +54,18 @@ rho_q ,beta_pdf,0.4,0.2;
rho_A ,beta_pdf,0.5,0.2;
rho_ys ,beta_pdf,0.8,0.1;
rho_pies,beta_pdf,0.7,0.15;
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
/*
stderr e_R,inv_gamma_pdf,(1.2533),(0.6551);
stderr e_q,inv_gamma_pdf,(2.5066),(1.3103);
stderr e_A,inv_gamma_pdf,(1.2533),(0.6551);
stderr e_ys,inv_gamma_pdf,(1.2533),(0.6551);
stderr e_pies,inv_gamma_pdf,(1.88),(0.9827);
*/
stderr e_R,inv_gamma_pdf,(1.2533/3),(0.6551/10);
stderr e_q,inv_gamma_pdf,(2.5066/3),(1.3103/10);
stderr e_A,inv_gamma_pdf,(1.2533/3),(0.6551/10);
stderr e_ys,inv_gamma_pdf,(1.2533/3),(0.6551/10);
stderr e_pies,inv_gamma_pdf,(1.88/3),(0.9827/10);
end;
// endogenous prior restrictions
@ -83,12 +90,21 @@ end;
moment_calibration;
//y_obs,y_obs, [0.8 1.1]; //[unconditional variance]
y_obs,y_obs(-(1:4)), +; //[first year acf]
@#for ilag in 0:1
y_obs,R_obs(-@{ilag}), -; //[ccf]
//y_obs,pie_obs(-4:4), -; //[ccf]
@#for ilag in -2:2
y_obs,R_obs(@{ilag}), -; //[ccf]
@#endfor
@#for ilag in -4:4
y_obs,pie_obs(@{ilag}), -; //[ccf]
@#endfor
end;
dynare_sensitivity(prior_range=0);
if isoctave()
dynare_sensitivity(prior_range=0, nodisplay, graph_format=(eps));
else
dynare_sensitivity(prior_range=0, nodisplay, graph_format=(fig));
end
/*
estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2, mode_file = ls2003a_mode,
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=0, mh_drop=0.6, bayesian_irf);

133
tests/optimizers/fs2000.common.inc Normal file
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@ -0,0 +1,133 @@
/*
* This file replicates the estimation of the cash in advance model described
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
* Journal of Applied Econometrics, 15(6), 645-670.
*
* The data are in file "fs2000/fsdat_simul.m", and have been artificially generated.
* They are therefore different from the original dataset used by Schorfheide.
*
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
* implications of long-run neutrality for monetary business cycle models",
* Journal of Applied Econometrics, 9, S37-S70.
* Note that there is an initial minus sign missing in equation (A1), p. S63.
*
* This implementation was written by Michel Juillard. Please note that the
* following copyright notice only applies to this Dynare implementation of the
* model.
*/
/*
* Copyright (C) 2004-2010 Dynare Team
*
* This file is part of Dynare.
*
* Dynare is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Dynare is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
*/
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp bet gam mst rho psi del;
alp = 0.33;
bet = 0.99;
gam = 0.003;
mst = 1.011;
rho = 0.7;
psi = 0.787;
del = 0.02;
model;
dA = exp(gam+e_a);
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
W = l/n;
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
P*c = m;
m-1+d = l;
e = exp(e_a);
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
steady_state_model;
dA = exp(gam);
gst = 1/dA;
m = mst;
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
n = xist/(nust+xist);
P = xist + nust;
k = khst*n;
l = psi*mst*n/( (1-psi)*(1-n) );
c = mst/P;
d = l - mst + 1;
y = k^alp*n^(1-alp)*gst^alp;
R = mst/bet;
W = l/n;
ist = y-c;
q = 1 - d;
e = 1;
gp_obs = m/dA;
gy_obs = dA;
end;
steady;
check;
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
end;
varobs gp_obs gy_obs;
options_.plot_priors=0;

5
tests/optimizers/fs2000_1.mod Normal file
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@ -0,0 +1,5 @@
@#include "fs2000.common.inc"
if ~isoctave() && exist('fmincon','file')
estimation(mode_compute=1,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);
end

3
tests/optimizers/fs2000_10.mod Normal file
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@ -0,0 +1,3 @@
@#include "fs2000.common.inc"
estimation(mode_compute=10,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);

3
tests/optimizers/fs2000_101.mod Normal file
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@ -0,0 +1,3 @@
@#include "fs2000.common.inc"
estimation(mode_compute=101,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);

5
tests/optimizers/fs2000_102.mod Normal file
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@ -0,0 +1,5 @@
@#include "fs2000.common.inc"
if ~isoctave() && exist('simulannealbnd','file')
estimation(mode_compute=102,mode_file=fs2000_mode,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0, mh_nblocks=2, mh_jscale=0.8);
end

3
tests/optimizers/fs2000_2.mod Normal file
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@ -0,0 +1,3 @@
@#include "fs2000.common.inc"
estimation(mode_compute=2,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);

5
tests/optimizers/fs2000_3.mod Normal file
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@ -0,0 +1,5 @@
@#include "fs2000.common.inc"
if exist('fminunc','file')
estimation(mode_compute=3,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);
end

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