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Merge branch 'master' into json

time-shift
Houtan Bastani 2017-03-03 14:42:25 +01:00
parent f500b8385d 33b38ab643
commit 5063020c5d
23 changed files with 257 additions and 203 deletions
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16
doc/dynare.texi
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@ -5410,6 +5410,10 @@ Uses the simpsa algorithm, based on the combination of the non-linear simplex an
@item 11
This is not strictly speaking an optimization algorithm. The (estimated) parameters are treated as state variables and estimated jointly with the original state variables of the model using a nonlinear filter. The algorithm implemented in Dynare is described in @cite{Liu and West (2001)}.
@item 12
Uses @code{particleswarm} optimization routine (available under MATLAB if
the Global Optimization Toolbox is installed; not available under Octave).
@item 101
Uses the SolveOpt algorithm for local nonlinear optimization problems proposed by
@cite{Kuntsevich and Kappel (1997)}.
@ -5513,7 +5517,7 @@ A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the o
@table @code
@item 1, 3, 7
@item 1, 3, 7, 12
Available options are given in the documentation of the MATLAB Optimization Toolbox or in Octave's documentation.
@item 2
@ -6165,6 +6169,16 @@ This is the convergence criterion used in the fixed point Lyapunov solver. Its d
@anchor{lyapunov_doubling_tol}
This is the convergence criterion used in the doubling algorithm to solve the Lyapunov equation. Its default value is 1e-16.
@item use_penalized_objective_for_hessian
Use the penalized objective instead of the objective function to compute
numerically the hessian matrix at the mode. The penalties decrease the value of
the posterior density (or likelihood) when, for some perturbations, Dynare is
not able to solve the model (issues with steady state existence, Blanchard and
Kahn conditions, ...). In pratice, the penalized and original
objectives will only differ if the posterior mode is found to be near a region
where the model is ill-behaved. By default the original objective function is
used.
@item analytic_derivation
Triggers estimation with analytic gradient. The final hessian is also
computed analytically. Only works for stationary models without

5
matlab/dsge_likelihood.m
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@ -141,7 +141,10 @@ trend_coeff = [];
exit_flag = 1;
info = zeros(4,1);
DLIK = [];
Hess = [];
Hess = [];
% Ensure that xparam1 is a column vector.
xparam1 = xparam1(:);
if DynareOptions.estimation_dll
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...

5
matlab/dsge_var_likelihood.m
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@ -37,7 +37,7 @@ function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2016 Dynare Team
% Copyright (C) 2006-2017 Dynare Team
%
% This file is part of Dynare.
%
@ -68,6 +68,9 @@ iXX = [];
prior = [];
trend_coeff=[];
% Ensure that xparam1 is a column vector.
xparam1 = xparam1(:);
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
if isempty(dsge_prior_weight_idx)
dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);

9
matlab/dynare_config.m
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@ -91,6 +91,15 @@ if isoctave
p{end+1} = '/missing/ordeig';
end
if isoctave && ~isequal(supported_octave_version(), version())
skipline()
warning(['This version of Octave is not supported. Consider installing ' ...
'version %s of Octave,\notherwise m files will be used instead ' ...
'of precompiled mex files and some features, like solution\n' ...
'of models approximated at third order, will not be available.'], supported_octave_version())
skipline()
end
% ilu is missing in Octave < 4.0
if isoctave && octave_ver_less_than('4.0')
p{end+1} = '/missing/ilu';

12
matlab/dynare_estimation_1.m
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@ -220,10 +220,14 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
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(penalty_hessian(objective_function,xparam1,fval, ...
options_.gstep,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_),nx,nx);
% with flag==0, we force to use the hessian from outer product gradient of optimizer 5
if options_.hessian.use_penalized_objective
penalized_objective_function = str2func('penalty_objective_function');
hh = hessian(penalized_objective_function, xparam1, options_.gstep, objective_function, fval, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_);
else
hh = hessian(objective_function, xparam1, options_.gstep, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_);
end
hh = reshape(hh, nx, nx);
elseif isnumeric(options_.mode_compute) && isequal(options_.mode_compute,5)
% other numerical hessian options available with optimizer 5
%

22
matlab/global_initialization.m
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@ -565,6 +565,11 @@ options_.homotopy_mode = 0;
options_.homotopy_steps = 1;
options_.homotopy_force_continue = 0;
% numerical hessian
hessian.use_penalized_objective = false;
options_.hessian = hessian;
%csminwel optimization routine
csminwel.tolerance.f=1e-7;
csminwel.maxiter=1000;
@ -645,6 +650,23 @@ options_.saopt.nt=10;
options_.saopt.step_length_c=0.1;
options_.saopt.initial_step_length=1;
% particleswarm (global optimization toolbox needed)
particleswarm.Display = 'iter';
particleswarm.DisplayInterval = 1;
particleswarm.FunctionTolerance = 1e-6;
particleswarm.FunValCheck = 'on';
particleswarm.HybridFcn = [];
particleswarm.InertiaRange = [0.1, 1.1];
particleswarm.MaxIterations = 100000;
particleswarm.MaxStallIterations = 20;
particleswarm.MaxStallTime = Inf;
particleswarm.MaxTime = Inf;
particleswarm.MinNeighborsFraction = .25;
particleswarm.ObjectiveLimit = -Inf;
particleswarm.UseParallel = false;
particleswarm.UseVectorized = false;
options_.particleswarm = particleswarm;
% prior analysis
options_.prior_mc = 20000;
options_.prior_analysis_endo_var_list = [];

77
matlab/hessian.m
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@ -1,4 +1,4 @@
function hessian_mat = hessian(func,x,gstep,varargin) % --*-- Unitary tests --*--
function hessian_mat = hessian(func,x, gstep, varargin) % --*-- Unitary tests --*--
% Computes second order partial derivatives
%
@ -26,7 +26,7 @@ function hessian_mat = hessian(func,x,gstep,varargin) % --*-- Unitary tests --*-
% none
%
% Copyright (C) 2001-2014 Dynare Team
% Copyright (C) 2001-2017 Dynare Team
%
% This file is part of Dynare.
%
@ -46,48 +46,55 @@ function hessian_mat = hessian(func,x,gstep,varargin) % --*-- Unitary tests --*-
if ~isa(func, 'function_handle')
func = str2func(func);
end
n=size(x,1);
h1=max(abs(x),sqrt(gstep(1))*ones(n,1))*eps^(1/6)*gstep(2);
h_1=h1;
xh1=x+h1;
h1=xh1-x;
xh1=x-h_1;
h_1=x-xh1;
xh1=x;
f0=feval(func,x,varargin{:});
f1=zeros(size(f0,1),n);
f_1=f1;
n = size(x,1);
h1 = max(abs(x), sqrt(gstep(1))*ones(n, 1))*eps^(1/6)*gstep(2);
h_1 = h1;
xh1 = x+h1;
h1 = xh1-x;
xh1 = x-h_1;
h_1 = x-xh1;
xh1 = x;
f0 = feval(func, x, varargin{:});
f1 = zeros(size(f0, 1), n);
f_1 = f1;
for i=1:n
%do step up
xh1(i)=x(i)+h1(i);
f1(:,i)=feval(func,xh1,varargin{:});
%do step up
xh1(i)=x(i)-h_1(i);
f_1(:,i)=feval(func,xh1,varargin{:});
xh1(i)=x(i);%reset parameter
xh1(i) = x(i)+h1(i);
f1(:,i) = feval(func, xh1, varargin{:});
%do step down
xh1(i) = x(i)-h_1(i);
f_1(:,i) = feval(func, xh1, varargin{:});
%reset parameter
xh1(i) = x(i);
end
xh_1=xh1;
hessian_mat = zeros(size(f0,1),n*n);
temp=f1+f_1-f0*ones(1,n); %term f_(1,0)+f_(-1,0)-f_(0,0) used later
xh_1 = xh1;
temp = f1+f_1-f0*ones(1, n); %term f_(1,0)+f_(-1,0)-f_(0,0) used later
hessian_mat = zeros(size(f0,1), n*n);
for i=1:n
if i > 1 %fill symmetric part of Hessian based on previously computed results
k=[i:n:n*(i-1)];
hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
if i > 1
%fill symmetric part of Hessian based on previously computed results
k = [i:n:n*(i-1)];
hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1) = hessian_mat(:,k);
end
hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i)); %formula 25.3.23
hessian_mat(:,(i-1)*n+i) = (f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i)); %formula 25.3.23
for j=i+1:n
%step in up direction
xh1(i)=x(i)+h1(i);
xh1(j)=x(j)+h_1(j);
xh1(i) = x(i)+h1(i);
xh1(j) = x(j)+h_1(j);
%step in down direction
xh_1(i)=x(i)-h1(i);
xh_1(j)=x(j)-h_1(j);
hessian_mat(:,(i-1)*n+j)=-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j)); %formula 25.3.27
xh_1(i) = x(i)-h1(i);
xh_1(j) = x(j)-h_1(j);
hessian_mat(:,(i-1)*n+j) =-(-feval(func, xh1, varargin{:})-feval(func, xh_1, varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j)); %formula 25.3.27
%reset grid points
xh1(i)=x(i);
xh1(j)=x(j);
xh_1(i)=x(i);
xh_1(j)=x(j);
xh1(i) = x(i);
xh1(j) = x(j);
xh_1(i) = x(i);
xh_1(j) = x(j);
end
end

8
matlab/non_linear_dsge_likelihood.m
View File

@ -107,7 +107,7 @@ function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,Model,DynareOptions,Bayes
%! @end deftypefn
%@eod:
% Copyright (C) 2010-2016 Dynare Team
% Copyright (C) 2010-2017 Dynare Team
%
% This file is part of Dynare.
%
@ -124,9 +124,6 @@ function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,Model,DynareOptions,Bayes
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH lemans DOT fr
% Declaration of the penalty as a persistent variable.
persistent init_flag
persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
@ -140,6 +137,9 @@ exit_flag = 1;
DLIK = [];
Hess = [];
% Ensure that xparam1 is a column vector.
xparam1 = xparam1(:);
% Issue an error if loglinear option is used.
if DynareOptions.loglinear
error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')

54
matlab/optimization/dynare_minimize_objective.m
View File

@ -401,8 +401,58 @@ switch minimizer_algorithm
[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{:}) ;
options_.cova_compute = 0;
[opt_par_values, stdh, lb_95, ub_95, med_param] = online_auxiliary_filter(start_par_value, varargin{:});
case 12
[LB, UB] = set_bounds_to_finite_values(bounds, options_.huge_number);
tmp = transpose([fieldnames(options_.particleswarm), struct2cell(options_.particleswarm)]);
particleswarmOptions = optimoptions(@particleswarm);
particleswarmOptions = optimoptions(particleswarmOptions, tmp{:});
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
SupportedListOfOptions = {'CreationFcn', 'Display', 'DisplayInterval', 'FunctionTolerance', ...
'FunValCheck', 'HybridFcn', 'InertiaRange', 'InitialSwarmMatrix', 'InitialSwarmSpan', ...
'MaxIterations', 'MaxStallIterations', 'MaxStallTime', 'MaxTime', ...
'MinNeighborsFraction', 'ObjectiveLimit', 'OutputFcn', 'PlotFcn', 'SelfAdjustmentWeight', ...
'SocialAdjustmentWeight', 'SwarmSize', 'UseParallel', 'UseVectorized'};
for i=1:rows(options_list)
if ismember(options_list{i,1}, SupportedListOfOptions)
particleswarmOptions = optimoptions(particleswarmOptions, options_list{i,1}, options_list{i,2});
else
warning(['particleswarm: Unknown option (' options_list{i,1} ')!'])
end
end
end
% Get number of instruments.
numberofvariables = length(start_par_value);
% Set objective function.
objfun = @(x) objective_function(x, varargin{:});
if ischar(particleswarmOptions.SwarmSize)
eval(['particleswarmOptions.SwarmSize = ' particleswarmOptions.SwarmSize ';'])
end
if isempty(particleswarmOptions.InitialSwarmMatrix)
particleswarmOptions.InitialSwarmMatrix = zeros(particleswarmOptions.SwarmSize, numberofvariables);
p = 1;
FVALS = zeros(particleswarmOptions.SwarmSize, 1);
while p<=particleswarmOptions.SwarmSize
candidate = rand(numberofvariables, 1).*(UB-LB)+LB;
[fval, info, exit_flag] = objfun(candidate);
if exit_flag
particleswarmOptions.InitialSwarmMatrix(p,:) = transpose(candidate);
FVALS(p) = fval;
p = p + 1;
end
end
end
% Set penalty to the worst value of the objective function.
TMP = [particleswarmOptions.InitialSwarmMatrix, FVALS];
TMP = sortrows(TMP, length(start_par_value)+1);
penalty = TMP(end,end);
% Define penalized objective.
objfun = @(x) penalty_objective_function(x, objective_function, penalty, varargin{:});
% Minimize the penalized objective (note that the penalty is not updated).
[opt_par_values, fval, exitflag, output] = particleswarm(objfun, length(start_par_value), LB, UB, particleswarmOptions);
opt_par_values = opt_par_values(:);
case 101
solveoptoptions = options_.solveopt;
if ~isempty(options_.optim_opt)

94
matlab/optimization/penalty_hessian.m
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@ -1,94 +0,0 @@
function hessian_mat = penalty_hessian(func,x,penalty,gstep,varargin) % --*-- Unitary tests --*--
% Computes second order partial derivatives with penalty_objective_function
%
% INPUTS
% func [string] name of the function
% x [double] vector, the Hessian of "func" is evaluated at x.
% penalty [double] penalty base used if function fails
% gstep [double] scalar, size of epsilon.
% varargin [void] list of additional arguments for "func".
%
% OUTPUTS
% hessian_mat [double] Hessian matrix
%
% ALGORITHM
% Uses Abramowitz and Stegun (1965) formulas 25.3.23
% \[
% \frac{\partial^2 f_{0,0}}{\partial {x^2}} = \frac{1}{h^2}\left( f_{1,0} - 2f_{0,0} + f_{ - 1,0} \right)
% \]
% and 25.3.27 p. 884
%
% \[
% \frac{\partial ^2f_{0,0}}{\partial x\partial y} = \frac{-1}{2h^2}\left(f_{1,0} + f_{-1,0} + f_{0,1} + f_{0,-1} - 2f_{0,0} - f_{1,1} - f_{-1,-1} \right)
% \]
%
% SPECIAL REQUIREMENTS
% none
%
% Copyright (C) 2001-2014 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/>.
if ~isa(func, 'function_handle')
func = str2func(func);
end
n=size(x,1);
h1=max(abs(x),sqrt(gstep(1))*ones(n,1))*eps^(1/6)*gstep(2);
h_1=h1;
xh1=x+h1;
h1=xh1-x;
xh1=x-h_1;
h_1=x-xh1;
xh1=x;
f0=penalty_objective_function(x,func,penalty,varargin{:});
f1=zeros(size(f0,1),n);
f_1=f1;
for i=1:n
%do step up
xh1(i)=x(i)+h1(i);
f1(:,i)=penalty_objective_function(xh1,func,penalty,varargin{:});
%do step up
xh1(i)=x(i)-h_1(i);
f_1(:,i)=penalty_objective_function(xh1,func,penalty,varargin{:});
xh1(i)=x(i);%reset parameter
end
xh_1=xh1;
hessian_mat = zeros(size(f0,1),n*n);
temp=f1+f_1-f0*ones(1,n); %term f_(1,0)+f_(-1,0)-f_(0,0) used later
for i=1:n
if i > 1 %fill symmetric part of Hessian based on previously computed results
k=[i:n:n*(i-1)];
hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
end
hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i)); %formula 25.3.23
for j=i+1:n
%step in up direction
xh1(i)=x(i)+h1(i);
xh1(j)=x(j)+h_1(j);
%step in down direction
xh_1(i)=x(i)-h1(i);
xh_1(j)=x(j)-h_1(j);
hessian_mat(:,(i-1)*n+j)=-(-penalty_objective_function(xh1,func,penalty,varargin{:})-penalty_objective_function(xh_1,func,penalty,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j)); %formula 25.3.27
%reset grid points
xh1(i)=x(i);
xh1(j)=x(j);
xh_1(i)=x(i);
xh_1(j)=x(j);
end
end

25
matlab/optimization/penalty_objective_function.m
View File

@ -1,7 +1,19 @@
function [fval,exit_flag,arg1,arg2] = penalty_objective_function(x0,fcn,penalty,varargin)
%function [fval,exit_flag,arg1,arg2] = penalty_objective_function(x0,fcn,penalty,varargin)
function [fval, exit_flag, arg1, arg2] = penalty_objective_function(x, fcn, base_penalty, varargin)
% Copyright (C) 2016 Dynare Team
% Encapsulates an objective function to be minimized, adding a penalty if necessary.
%
% INPUTS
% - x [double] n*1 vector of instrument values.
% - fcn [fhandle] objective function.
% - base_penalty [double] scalar, base of the penality (typically the value of the objective at the previous iteration).
% - varagin [cell] additional parameters for fcn.
%
% OUTPUTS
% - fval [double] scalar, value of the objective function at x.
% - exit_flag [integer] scalar, flag returned by fcn (third output).
% - arg1, arg2 fourth and fifth output arguments of the objective function.
% Copyright (C) 2016-2017 Dynare Team
%
% This file is part of Dynare.
%
@ -18,9 +30,8 @@ function [fval,exit_flag,arg1,arg2] = penalty_objective_function(x0,fcn,penalty,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[fval,info,exit_flag,arg1,arg2] = fcn(x0,varargin{:});
[fval, info, exit_flag, arg1, arg2] = fcn(x, varargin{:});
if info(1) ~= 0
fval = penalty + info(4);
end
if info(1)
fval = base_penalty + info(4);
end

5
matlab/resol.m
View File

@ -109,9 +109,10 @@ if info(1)
end
if options.loglinear
threshold = 1e-16;
% Find variables with non positive steady state. Skip auxiliary
% variables for lagges/leaded exogenous variables
idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M)) < 1e-9);
idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M))<threshold);
if length(idx)
if options.debug
variables_with_non_positive_steady_state = M.endo_names(idx,:);
@ -132,7 +133,7 @@ if options.loglinear
end
end
info(1)=26;
info(2)=sum(dr.ys(dr.ys<1e-9).^2);
info(2)=sum(dr.ys(dr.ys<threshold).^2);
return
end
end

20
matlab/supported_octave_version.m Normal file
View File

@ -0,0 +1,20 @@
function v = supported_octave_version()
% Copyright (C) 2017 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/>.
v = '4.2.0';

2
mex/build/bytecode.am
View File

@ -1,6 +1,6 @@
mex_PROGRAMS = bytecode
bytecode_CPPFLAGS = $(AM_CPPFLAGS) -I$(top_srcdir)/../../sources -I$(top_srcdir)/../../sources/bytecode -I$(top_srcdir)/../../../preprocessor
bytecode_CPPFLAGS = -Wno-maybe-uninitialized $(AM_CPPFLAGS) -I$(top_srcdir)/../../sources -I$(top_srcdir)/../../sources/bytecode -I$(top_srcdir)/../../../preprocessor
TOPDIR = $(top_srcdir)/../../sources/bytecode

21
preprocessor/ComputingTasks.cc
View File

@ -470,7 +470,7 @@ RamseyConstraintsStatement::writeOutput(ostream &output, const string &basename,
break;
default:
cerr << "Ramsey constraints: this shouldn't happen." << endl;
exit(1);
exit(EXIT_FAILURE);
}
output << "', '";
it->expression->writeOutput(output);
@ -1094,9 +1094,7 @@ EstimatedParamsStatement::writeOutput(ostream &output, const string &basename, b
<< "estim_params_.corrn = [];" << endl
<< "estim_params_.param_vals = [];" << endl;
vector<EstimationParams>::const_iterator it;
for (it = estim_params_list.begin(); it != estim_params_list.end(); it++)
for (vector<EstimationParams>::const_iterator it = estim_params_list.begin(); it != estim_params_list.end(); it++)
{
int symb_id = symbol_table.getTypeSpecificID(it->name) + 1;
SymbolType symb_type = symbol_table.getType(it->name);
@ -1214,9 +1212,7 @@ EstimatedParamsInitStatement::writeOutput(ostream &output, const string &basenam
if (use_calibration)
output << "options_.use_calibration_initialization = 1;" << endl;
vector<EstimationParams>::const_iterator it;
for (it = estim_params_list.begin(); it != estim_params_list.end(); it++)
for (vector<EstimationParams>::const_iterator it = estim_params_list.begin(); it != estim_params_list.end(); it++)
{
int symb_id = symbol_table.getTypeSpecificID(it->name) + 1;
SymbolType symb_type = symbol_table.getType(it->name);
@ -1313,9 +1309,7 @@ EstimatedParamsBoundsStatement::EstimatedParamsBoundsStatement(const vector<Esti
void
EstimatedParamsBoundsStatement::writeOutput(ostream &output, const string &basename, bool minimal_workspace) const
{
vector<EstimationParams>::const_iterator it;
for (it = estim_params_list.begin(); it != estim_params_list.end(); it++)
for (vector<EstimationParams>::const_iterator it = estim_params_list.begin(); it != estim_params_list.end(); it++)
{
int symb_id = symbol_table.getTypeSpecificID(it->name) + 1;
SymbolType symb_type = symbol_table.getType(it->name);
@ -1436,10 +1430,7 @@ void
ObservationTrendsStatement::writeOutput(ostream &output, const string &basename, bool minimal_workspace) const
{
output << "options_.trend_coeff = {};" << endl;
trend_elements_t::const_iterator it;
for (it = trend_elements.begin(); it != trend_elements.end(); it++)
for (trend_elements_t::const_iterator it = trend_elements.begin(); it != trend_elements.end(); it++)
{
SymbolType type = symbol_table.getType(it->first);
if (type == eEndogenous)
@ -1450,7 +1441,7 @@ ObservationTrendsStatement::writeOutput(ostream &output, const string &basename,
output << "';" << endl;
}
else
cout << "Error : Non-variable symbol used in TREND_COEFF: " << it->first << endl;
cerr << "Warning : Non-variable symbol used in observation_trends: " << it->first << endl;
}
}

20
preprocessor/DynamicModel.cc
View File

@ -800,7 +800,7 @@ DynamicModel::writeModelEquationsCode(string &file_name, const string &bin_basen
code_file.open(main_name.c_str(), ios::out | ios::binary | ios::ate);
if (!code_file.is_open())
{
cout << "Error : Can't open file \"" << main_name << "\" for writing\n";
cerr << "Error : Can't open file \"" << main_name << "\" for writing" << endl;
exit(EXIT_FAILURE);
}
@ -1076,7 +1076,7 @@ DynamicModel::writeModelEquationsCode_Block(string &file_name, const string &bin
code_file.open(main_name.c_str(), ios::out | ios::binary | ios::ate);
if (!code_file.is_open())
{
cout << "Error : Can't open file \"" << main_name << "\" for writing\n";
cerr << "Error : Can't open file \"" << main_name << "\" for writing" << endl;
exit(EXIT_FAILURE);
}
//Temporary variables declaration
@ -1768,7 +1768,7 @@ DynamicModel::Write_Inf_To_Bin_File_Block(const string &dynamic_basename, const
SaveCode.open((bin_basename + "_dynamic.bin").c_str(), ios::out | ios::binary);
if (!SaveCode.is_open())
{
cout << "Error : Can't open file \"" << bin_basename << "_dynamic.bin\" for writing\n";
cerr << "Error : Can't open file \"" << bin_basename << "_dynamic.bin\" for writing" << endl;
exit(EXIT_FAILURE);
}
u_count_int = 0;
@ -3252,8 +3252,8 @@ DynamicModel::computingPass(bool jacobianExo, bool hessian, bool thirdDerivative
if (!no_tmp_terms)
computeTemporaryTermsOrdered();
int k = 0;
equation_block = vector<int>(equation_number());
variable_block_lead_lag = vector< pair< int, pair< int, int> > >(equation_number());
equation_block = vector<int>(equations.size());
variable_block_lead_lag = vector< pair< int, pair< int, int> > >(equations.size());
for (unsigned int i = 0; i < getNbBlocks(); i++)
{
for (unsigned int j = 0; j < getBlockSize(i); j++)
@ -4210,10 +4210,10 @@ DynamicModel::writeParamsDerivativesFile(const string &basename, bool julia) con
<< "% from model file (.mod)" << endl << endl
<< model_local_vars_output.str()
<< model_output.str()
<< "rp = zeros(" << equation_number() << ", "
<< "rp = zeros(" << equations.size() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< jacobian_output.str()
<< "gp = zeros(" << equation_number() << ", " << dynJacobianColsNbr << ", " << symbol_table.param_nbr() << ");" << endl
<< "gp = zeros(" << equations.size() << ", " << dynJacobianColsNbr << ", " << symbol_table.param_nbr() << ");" << endl
<< hessian_output.str()
<< "if nargout >= 3" << endl
<< "rpp = zeros(" << residuals_params_second_derivatives.size() << ",4);" << endl
@ -4238,10 +4238,10 @@ DynamicModel::writeParamsDerivativesFile(const string &basename, bool julia) con
<< "ss_param_deriv, ss_param_2nd_deriv)" << endl
<< model_local_vars_output.str()
<< model_output.str()
<< "rp = zeros(" << equation_number() << ", "
<< "rp = zeros(" << equations.size() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< jacobian_output.str()
<< "gp = zeros(" << equation_number() << ", " << dynJacobianColsNbr << ", " << symbol_table.param_nbr() << ");" << endl
<< "gp = zeros(" << equations.size() << ", " << dynJacobianColsNbr << ", " << symbol_table.param_nbr() << ");" << endl
<< hessian_output.str()
<< "rpp = zeros(" << residuals_params_second_derivatives.size() << ",4);" << endl
<< hessian1_output.str()
@ -5012,7 +5012,7 @@ DynamicModel::writeFirstDerivativesC_csr(const string &basename, bool cuda) cons
break;
default:
std::cerr << "This case shouldn't happen" << std::endl;
exit(1);
exit(EXIT_FAILURE);
}
derivative deriv(col_id + eq*cols_nbr,col_id,eq,it->second);
D.push_back(deriv);

3
preprocessor/DynareBison.yy
View File

@ -113,6 +113,7 @@ class ParsingDriver;
%token CPF_WEIGHTS AMISANOTRISTANI MURRAYJONESPARSLOW
%token FILTER_ALGORITHM PROPOSAL_APPROXIMATION CUBATURE UNSCENTED MONTECARLO DISTRIBUTION_APPROXIMATION
%token <string_val> NAME
%token USE_PENALIZED_OBJECTIVE_FOR_HESSIAN
%token NAN_CONSTANT NO_STATIC NOBS NOCONSTANT NODISPLAY NOCORR NODIAGNOSTIC NOFUNCTIONS NO_HOMOTOPY
%token NOGRAPH POSTERIOR_NOGRAPH POSTERIOR_GRAPH NOMOMENTS NOPRINT NORMAL_PDF SAVE_DRAWS
%token OBSERVATION_TRENDS OPTIM OPTIM_WEIGHTS ORDER OSR OSR_PARAMS MAX_DIM_COVA_GROUP ADVANCED OUTFILE OUTVARS OVERWRITE
@ -1825,6 +1826,7 @@ estimation_options : o_datafile
| o_posterior_sampling_method
| o_posterior_sampler_options
| o_keep_kalman_algo_if_singularity_is_detected
| o_use_penalized_objective_for_hessian
;
list_optim_option : QUOTED_STRING COMMA QUOTED_STRING
@ -3232,6 +3234,7 @@ o_mcmc_jumping_covariance : MCMC_JUMPING_COVARIANCE EQUAL HESSIAN
| MCMC_JUMPING_COVARIANCE EQUAL filename
{ driver.option_str("MCMC_jumping_covariance", $3); }
;
o_use_penalized_objective_for_hessian : USE_PENALIZED_OBJECTIVE_FOR_HESSIAN { driver.option_num("hessian.use_penalized_objective","true"); };
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"); };

1
preprocessor/DynareFlex.ll
View File

@ -400,6 +400,7 @@ 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>distribution_approximation {return token::DISTRIBUTION_APPROXIMATION;}
<DYNARE_STATEMENT>proposal_distribution {return token::PROPOSAL_DISTRIBUTION;}
<DYNARE_STATEMENT>no_posterior_kernel_density {return token::NO_POSTERIOR_KERNEL_DENSITY;}
<DYNARE_STATEMENT>use_penalized_objective_for_hessian {return token::USE_PENALIZED_OBJECTIVE_FOR_HESSIAN;}
<DYNARE_STATEMENT>alpha {
yylval->string_val = new string(yytext);

22
preprocessor/ModelTree.cc
View File

@ -36,7 +36,7 @@ using namespace MFS;
bool
ModelTree::computeNormalization(const jacob_map_t &contemporaneous_jacobian, bool verbose)
{
const int n = equation_number();
const int n = equations.size();
assert(n == symbol_table.endo_nbr());
@ -96,7 +96,7 @@ ModelTree::computeNormalization(const jacob_map_t &contemporaneous_jacobian, boo
#endif
// Create the resulting map, by copying the n first elements of mate_map, and substracting n to them
endo2eq.resize(equation_number());
endo2eq.resize(equations.size());
transform(mate_map.begin(), mate_map.begin() + n, endo2eq.begin(), bind2nd(minus<int>(), n));
#ifdef DEBUG
@ -143,7 +143,7 @@ ModelTree::computeNonSingularNormalization(jacob_map_t &contemporaneous_jacobian
cout << "Normalizing the model..." << endl;
int n = equation_number();
int n = equations.size();
// compute the maximum value of each row of the contemporaneous Jacobian matrix
//jacob_map normalized_contemporaneous_jacobian;
@ -329,7 +329,7 @@ ModelTree::writeRevXrefs(ostream &output, const map<int, set<int> > &xrefmap, co
void
ModelTree::computeNormalizedEquations(multimap<int, int> &endo2eqs) const
{
for (int i = 0; i < equation_number(); i++)
for (int i = 0; i < equations.size(); i++)
{
VariableNode *lhs = dynamic_cast<VariableNode *>(equations[i]->get_arg1());
if (lhs == NULL)
@ -417,11 +417,11 @@ ModelTree::evaluateAndReduceJacobian(const eval_context_t &eval_context, jacob_m
void
ModelTree::computePrologueAndEpilogue(const jacob_map_t &static_jacobian_arg, vector<int> &equation_reordered, vector<int> &variable_reordered)
{
vector<int> eq2endo(equation_number(), 0);
equation_reordered.resize(equation_number());
variable_reordered.resize(equation_number());
vector<int> eq2endo(equations.size(), 0);
equation_reordered.resize(equations.size());
variable_reordered.resize(equations.size());
bool *IM;
int n = equation_number();
int n = equations.size();
IM = (bool *) calloc(n*n, sizeof(bool));
int i = 0;
for (vector<int>::const_iterator it = endo2eq.begin(); it != endo2eq.end(); it++, i++)
@ -1696,7 +1696,7 @@ ModelTree::Write_Inf_To_Bin_File(const string &basename,
SaveCode.open(bin_basename.c_str(), ios::out | ios::binary);
if (!SaveCode.is_open())
{
cout << "Error : Can't open file \"" << bin_basename << "\" for writing\n";
cerr << "Error : Can't open file \"" << bin_basename << "\" for writing" << endl;
exit(EXIT_FAILURE);
}
u_count_int = 0;
@ -1799,7 +1799,7 @@ ModelTree::addEquation(expr_t eq, int lineno)
void
ModelTree::addEquation(expr_t eq, int lineno, vector<pair<string, string> > &eq_tags)
{
int n = equation_number();
int n = equations.size();
for (size_t i = 0; i < eq_tags.size(); i++)
equation_tags.push_back(make_pair(n, eq_tags[i]));
addEquation(eq, lineno);
@ -1843,7 +1843,7 @@ ModelTree::addNonstationaryVariables(vector<int> nonstationary_vars, bool log_de
void
ModelTree::initializeVariablesAndEquations()
{
for (int j = 0; j < equation_number(); j++)
for (int j = 0; j < equations.size(); j++)
{
equation_reordered.push_back(j);
variable_reordered.push_back(j);

14
preprocessor/StaticModel.cc
View File

@ -412,7 +412,7 @@ StaticModel::writeModelEquationsCode(const string file_name, const string bin_ba
code_file.open(main_name.c_str(), ios::out | ios::binary | ios::ate);
if (!code_file.is_open())
{
cout << "Error : Can't open file \"" << main_name << "\" for writing\n";
cerr << "Error : Can't open file \"" << main_name << "\" for writing" << endl;
exit(EXIT_FAILURE);
}
int count_u;
@ -596,7 +596,7 @@ StaticModel::writeModelEquationsCode_Block(const string file_name, const string
code_file.open(main_name.c_str(), ios::out | ios::binary | ios::ate);
if (!code_file.is_open())
{
cout << "Error : Can't open file \"" << main_name << "\" for writing\n";
cerr << "Error : Can't open file \"" << main_name << "\" for writing" << endl;
exit(EXIT_FAILURE);
}
//Temporary variables declaration
@ -990,7 +990,7 @@ StaticModel::Write_Inf_To_Bin_File_Block(const string &static_basename, const st
SaveCode.open((bin_basename + "_static.bin").c_str(), ios::out | ios::binary);
if (!SaveCode.is_open())
{
cout << "Error : Can't open file \"" << bin_basename << "_static.bin\" for writing\n";
cerr << "Error : Can't open file \"" << bin_basename << "_static.bin\" for writing" << endl;
exit(EXIT_FAILURE);
}
u_count_int = 0;
@ -2368,10 +2368,10 @@ StaticModel::writeParamsDerivativesFile(const string &basename, bool julia) cons
<< "% from model file (.mod)" << endl << endl
<< model_local_vars_output.str()
<< model_output.str()
<< "rp = zeros(" << equation_number() << ", "
<< "rp = zeros(" << equations.size() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< jacobian_output.str()
<< "gp = zeros(" << equation_number() << ", " << symbol_table.endo_nbr() << ", "
<< "gp = zeros(" << equations.size() << ", " << symbol_table.endo_nbr() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< hessian_output.str()
<< "if nargout >= 3" << endl
@ -2396,10 +2396,10 @@ StaticModel::writeParamsDerivativesFile(const string &basename, bool julia) cons
<< "function params_derivs(y, x, params)" << endl
<< model_local_vars_output.str()
<< model_output.str()
<< "rp = zeros(" << equation_number() << ", "
<< "rp = zeros(" << equations.size() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< jacobian_output.str()
<< "gp = zeros(" << equation_number() << ", " << symbol_table.endo_nbr() << ", "
<< "gp = zeros(" << equations.size() << ", " << symbol_table.endo_nbr() << ", "
<< symbol_table.param_nbr() << ");" << endl
<< hessian_output.str()
<< "rpp = zeros(" << residuals_params_second_derivatives.size() << ",4);" << endl

3
preprocessor/StaticModel.hh
View File

@ -30,9 +30,6 @@ using namespace std;
class StaticModel : public ModelTree
{
private:
//! Temporary terms for the file containing parameters dervicatives
temporary_terms_t params_derivs_temporary_terms;
//! global temporary terms for block decomposed models
vector<vector<temporary_terms_t> > v_temporary_terms;

19
tests/optimizers/fs2000.common.inc
View File

@ -1,7 +1,7 @@
/*
* 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.
* This file replicates (except for a small difference in the priors)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.
@ -17,7 +17,7 @@
*/
/*
* Copyright (C) 2004-2010 Dynare Team
* Copyright (C) 2004-2017 Dynare Team
*
* This file is part of Dynare.
*
@ -106,7 +106,16 @@ 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;
/*
* The prior on the autoregressive parameter rho is different from the one considered in the original paper.
* We lowered the prior variance to ensure that the prior does not have an asymptote at zero, which causes
* troubles for some optimizers. For instance, mode_compute=12 (which relies on Mathworks' particleswarm
* routine) finds a posterior at zero for rho (with a much higher posterior density than the one reported
* by csminwell or other optimization routines). Because zero is on the boundary of the prior, this is an
* an issue when we have to compute the hessian matrix (which is not full rank) at the mode or if we try to
* use this solution as an initial guess for csminwell.
*/
rho, beta_pdf, 0.129, 0.100;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;

3
tests/optimizers/fs2000_12.mod Normal file
View File

@ -0,0 +1,3 @@
@#include "fs2000.common.inc"
estimation(mode_compute=12,order=1, datafile='../fs2000/fsdat_simul', nobs=192, mh_replic=0);