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- new Incidence_Matrix class 

- lead and lag on exogenous variables
- corrections in dr1_sparse and dr11_sparse
- minor corrections in simulate

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2255 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
ferhat 2008-11-14 16:07:47 +00:00
parent a02e66e12c
commit f904a1a19d
21 changed files with 1783 additions and 694 deletions
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234
matlab/disp_dr_sparse.m Normal file
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@ -0,0 +1,234 @@
function disp_dr_sparse(dr,order,var_list)
% Copyright (C) 2001 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/>.
global M_
nx = 0;
nu = 0;
k = [];
klag = [];
k1 = [];
nspred = 0;
for i=1:length(M_.block_structure.block)
nspred = nspred + M_.block_structure.block(i).dr.nspred;
end;
ghu = zeros(M_.endo_nbr, M_.exo_nbr*(M_.maximum_exo_lag+M_.maximum_exo_lead+1));
ghx = zeros(M_.endo_nbr, nspred);
for i=1:length(M_.block_structure.block)
nx = nx + size(M_.block_structure.block(i).dr.ghx,2);
% M_.block_structure.block(i).dr.ghx
% M_.block_structure.block(i).equation
% M_.block_structure.block(i).variable
ghx(M_.block_structure.block(i).equation, M_.block_structure.block(i).variable(find(M_.block_structure.block(i).lead_lag_incidence(1: M_.block_structure.block(i).maximum_endo_lag,:))) ) = M_.block_structure.block(i).dr.ghx;
if(M_.block_structure.block(i).exo_nbr)
nu = nu + size(M_.block_structure.block(i).dr.ghu,2);
ghu(M_.block_structure.block(i).equation, M_.block_structure.block(i).exogenous) = M_.block_structure.block(i).dr.ghu;
end
k_tmp = find(M_.block_structure.block(i).dr.kstate(:,2) <= M_.block_structure.block(i).maximum_lag+1);
k = [k ; k_tmp];
klag = [klag ; M_.block_structure.block(i).dr.kstate(k_tmp,[1 2])];
k1 = [k1 ; M_.block_structure.block(i).variable(M_.block_structure.block(i).dr.order_var)'];
end
nvar = size(var_list,1);
if nvar == 0
nvar = length(k1);
ivar = [1:nvar];
else
ivar=zeros(nvar,1);
for i=1:nvar
i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),'exact');
if isempty(i_tmp)
disp(var_list(i,:));
error (['One of the variable specified does not exist']) ;
else
ivar(i) = i_tmp;
end
end
end
disp('POLICY AND TRANSITION FUNCTIONS')
% variable names
str = ' ';
for i=1:nvar
str = [str sprintf('%16s',M_.endo_names(k1(ivar(i)),:))];
end
disp(str);
%
% constant
%
str = '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
end
if flag
disp(str)
end
if order > 1
str = '(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
end
if flag
disp(str)
end
end
%
% ghx
%
for k=1:nx
flag = 0;
str1 = sprintf('%s(%d)',M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2);
str = sprintf('%-20s',str1);
for i=1:nvar
x = ghx(ivar(i),k);
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
end
if flag
disp(str)
end
end
%
% ghu
%
for k=1:nu
flag = 0;
str = sprintf('%-20s',M_.exo_names(k,:));
for i=1:nvar
x = ghu(ivar(i),k);
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
else
str = [str ' 0'];
end
end
if flag
disp(str)
end
end
if order > 1
% ghxx
for k = 1:nx
for j = 1:k
flag = 0;
str1 = sprintf('%s(%d),%s(%d)',M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2, ...
M_.endo_names(k1(klag(j,1)),:),klag(j,2)-M_.maximum_lag-2);
str = sprintf('%-20s',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
end
if flag
disp(str)
end
end
end
%
% ghuu
%
for k = 1:nu
for j = 1:k
flag = 0;
str = sprintf('%-20s',[M_.exo_names(k,:) ',' 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
end
if flag
disp(str)
end
end
end
%
% ghxu
%
for k = 1:nx
for j = 1:nu
flag = 0;
str1 = sprintf('%s(%d),%s',M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2, ...
M_.exo_names(j,:));
str = sprintf('%-20s',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
end
if flag
disp(str)
end
end
end
end
% $$$ dr.ghx
% $$$ dr.ghu
% $$$ dr.ghxx
% $$$ dr.ghuu
% $$$ dr.ghxu
% 01/08/2001 MJ added test for order in printing quadratic terms
% 02/21/2001 MJ pass all variable names through deblank()
% 02/21/2001 MJ changed from f to g format to write numbers
% 10/09/2002 MJ corrected error on constant whith subset of variables

14
matlab/dr11_sparse.m
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@ -17,7 +17,7 @@ function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobi
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
%task
info = 0;
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
kstate = dr.kstate;
@ -46,7 +46,12 @@ function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobi
m = m+length(k);
end
if M_.exo_nbr & task~=1
jacobia_
jacobia_(:,nz+1:end)
b
dr.ghu = -b\jacobia_(:,nz+1:end);
disp(['nz=' int2str(nz) ]);
dr.ghu
end
dr.eigval = eig(transition_matrix(dr,M_));
dr.rank = 0;
@ -156,12 +161,15 @@ function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobi
k1 = find(kstate(n4:nd,2) == M_.maximum_endo_lag+1);
k2 = find(kstate(1:n3,2) == M_.maximum_endo_lag+2);
hx(k1,:)
gx(k2(nboth+1:end),:)
dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
dr.ghx
%lead variables actually present in the model
j3 = nonzeros(kstate(:,3));
j4 = find(kstate(:,3));
% derivatives with respect to exogenous variables
disp(['M_.exo_nbr=' int2str(M_.exo_nbr)]);
if M_.exo_nbr
fu = aa(:,nz+(1:M_.exo_nbr));
a1 = b;
@ -216,7 +224,6 @@ function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobi
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
end
end
disp('end0');
if options_.order == 1
return
end
@ -479,5 +486,4 @@ function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobi
end
end
disp('end');
end

30
matlab/dr1_sparse.m
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@ -210,23 +210,47 @@ function [dr,info,M_,options_,oo_] = dr1_sparse(dr,task,M_,options_,oo_)
if options_.order == 1
[junk,jacobia_] = feval([M_.fname '_dynamic'],ones(M_.maximum_lag+M_.maximum_lead+1,1)*dr.ys',[oo_.exo_simul ...
oo_.exo_det_simul], it_);
%full(jacobia_)
dr.eigval = [];
dr.nyf = 0;
dr.rank = 0;
first_col_exo = M_.endo_nbr * (M_.maximum_endo_lag + M_.maximum_endo_lead + 1);
for i=1:length(M_.block_structure.block)
%disp(['block = ' int2str(i)]);
M_.block_structure.block(i).dr.Null=0;
M_.block_structure.block(i).dr=set_state_space(M_.block_structure.block(i).dr,M_.block_structure.block(i));
col_selector=repmat(M_.block_structure.block(i).variable,1,M_.block_structure.block(i).maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead+1)+kron([M_.maximum_endo_lag-M_.block_structure.block(i).maximum_endo_lag:M_.maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead],M_.endo_nbr*ones(1,M_.block_structure.block(i).endo_nbr));
row_selector = M_.block_structure.block(i).equation;
%col_selector
jcb_=jacobia_(row_selector,col_selector);
jcb_ = jcb_(:,find(M_.block_structure.block(i).lead_lag_incidence')) ;
jcb_ = jcb_(:,find(M_.block_structure.block(i).lead_lag_incidence')) ;
if M_.block_structure.block(i).exo_nbr>0
col_selector = [ first_col_exo + ...
repmat(M_.block_structure.block(i).exogenous,1,M_.block_structure.block(i).maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead+1)+kron([M_.maximum_exo_lag-M_.block_structure.block(i).maximum_exo_lag:M_.maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead],M_.exo_nbr*ones(1,M_.block_structure.block(i).exo_nbr))];
end
%col_selector
jcb_ = [ jcb_ jacobia_(row_selector,col_selector)];
%full(jcb_)
hss_=0; %hessian(M_.block_structure.block(i).equation,M_.block_structure.block(i).variable);
dra = M_.block_structure.block(i).dr;
M_.block_structure.block(i).exo_nbr=M_.exo_nbr;
%M_.block_structure.block(i).exo_nbr=M_.exo_nbr;
[dra ,info,M_.block_structure.block(i),options_,oo_] = dr11_sparse(dra ,task,M_.block_structure.block(i),options_,oo_, jcb_, hss_);
M_.block_structure.block(i).dr = dra;
dr.eigval = [dr.eigval; dra.eigval];
dr.nyf = dr.nyf + nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1);
nyf = nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1);
n_explod = nnz(abs(dra.eigval) > options_.qz_criterium);
if nyf ~= n_explod
disp(['EIGENVALUES in block ' int2str(i) ':']);
[m_lambda,ii]=sort(abs(dra.eigval));
disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary'))
z=[m_lambda real(dra.eigval(ii)) imag(dra.eigval(ii))]';
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
disp(['The rank condition is not satisfy in block ' int2str(i) ' :']);
disp([' ' int2str(nyf) ' forward-looking variable(s) for ' ...
int2str(n_explod) ' eigenvalue(s) larger than 1 in modulus']);
end
dr.nyf = dr.nyf + nyf;
dr.rank = dr.rank + dra.rank;
end;
end

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matlab/stoch_simul_sparse.m Normal file
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function info=stoch_simul(var_list)
% Copyright (C) 2001-2008 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/>.
global M_ options_ oo_ it_
options_old = options_;
if options_.linear
options_.order = 1;
end
if (options_.order == 1)
options_.replic = 1;
end
TeX = options_.TeX;
iter_ = max(options_.periods,1);
if M_.exo_nbr > 0
oo_.exo_simul= ones(iter_ + M_.maximum_lag + M_.maximum_lead,1) * oo_.exo_steady_state';
end
check_model;
[oo_.dr, info] = resol(oo_.steady_state,0);
if info(1)
options_ = options_old;
print_info(info);
return
end
oo_dr_kstate = [];
oo_dr_nstatic = 0;
for i=1:length(M_.block_structure.block)
oo_dr_kstate = [ oo_dr_kstate ; M_.block_structure.block(i).dr.kstate];
oo_dr_nstatic = oo_dr_nstatic + M_.block_structure.block(i).dr.nstatic;
end
if ~options_.noprint
disp(' ')
disp('MODEL SUMMARY')
disp(' ')
disp([' Number of variables: ' int2str(M_.endo_nbr)])
disp([' Number of stochastic shocks: ' int2str(M_.exo_nbr)])
disp([' Number of state variables: ' ...
int2str(length(find(oo_dr_kstate(:,2) <= M_.maximum_lag+1)))])
disp([' Number of jumpers: ' ...
int2str(length(find(oo_dr_kstate(:,2) == M_.maximum_lag+2)))])
disp([' Number of static variables: ' int2str(oo_dr_nstatic)])
my_title='MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS';
labels = deblank(M_.exo_names);
headers = strvcat('Variables',labels);
lh = size(labels,2)+2;
table(my_title,headers,labels,M_.Sigma_e,lh,10,6);
disp(' ')
disp_dr_sparse(oo_.dr,options_.order,var_list);
end
if options_.simul == 0 & options_.nomoments == 0
disp_th_moments(oo_.dr,var_list);
elseif options_.simul == 1
if options_.periods == 0
error('STOCH_SIMUL error: number of periods for the simulation isn''t specified')
end
if options_.periods < options_.drop
disp(['STOCH_SIMUL error: The horizon of simulation is shorter' ...
' than the number of observations to be DROPed'])
options_ =options_old;
return
end
oo_.endo_simul = simult(repmat(oo_.dr.ys,1,M_.maximum_lag),oo_.dr);
dyn2vec;
if options_.nomoments == 0
disp_moments(oo_.endo_simul,var_list);
end
end
if options_.irf
n = size(var_list,1);
if n == 0
n = M_.endo_nbr;
ivar = [1:n]';
var_list = M_.endo_names;
if TeX
var_listTeX = M_.endo_names_tex;
end
else
ivar=zeros(n,1);
if TeX
var_listTeX = [];
end
for i=1:n
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
if isempty(i_tmp)
error (['One of the specified variables does not exist']) ;
else
ivar(i) = i_tmp;
if TeX
var_listTeX = strvcat(var_listTeX,deblank(M_.endo_names_tex(i_tmp,:)));
end
end
end
end
if TeX
fidTeX = fopen([M_.fname '_IRF.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by stoch_simul.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
olditer = iter_;% Est-ce vraiment utile ? Il y a la même ligne dans irf...
SS(M_.exo_names_orig_ord,M_.exo_names_orig_ord)=M_.Sigma_e+1e-14*eye(M_.exo_nbr);
cs = transpose(chol(SS));
tit(M_.exo_names_orig_ord,:) = M_.exo_names;
if TeX
titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
end
for i=1:M_.exo_nbr
if SS(i,i) > 1e-13
y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i), options_.irf, options_.drop, ...
options_.replic, options_.order);
if options_.relative_irf
y = 100*y/cs(i,i);
end
irfs = [];
mylist = [];
if TeX
mylistTeX = [];
end
for j = 1:n
assignin('base',[deblank(M_.endo_names(ivar(j),:)) '_' deblank(M_.exo_names(i,:))],...
y(ivar(j),:)');
eval(['oo_.irfs.' deblank(M_.endo_names(ivar(j),:)) '_' ...
deblank(M_.exo_names(i,:)) ' = y(ivar(j),:);']);
if max(y(ivar(j),:)) - min(y(ivar(j),:)) > 1e-10
irfs = cat(1,irfs,y(ivar(j),:));
mylist = strvcat(mylist,deblank(var_list(j,:)));
if TeX
mylistTeX = strvcat(mylistTeX,deblank(var_listTeX(j,:)));
end
end
end
if options_.nograph == 0
number_of_plots_to_draw = size(irfs,1);
[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
if nbplt == 0
elseif nbplt == 1
if options_.relative_irf
hh = figure('Name',['Relative response to' ...
' orthogonalized shock to ' tit(i,:)]);
else
hh = figure('Name',['Orthogonalized shock to' ...
' ' tit(i,:)]);
end
for j = 1:number_of_plots_to_draw
subplot(nr,nc,j);
plot(1:options_.irf,transpose(irfs(j,:)),'-k','linewidth',1);
hold on
plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
hold off
xlim([1 options_.irf]);
title(deblank(mylist(j,:)),'Interpreter','none');
end
eval(['print -depsc2 ' M_.fname '_IRF_' deblank(tit(i,:)) '.eps']);
if ~exist('OCTAVE_VERSION')
eval(['print -dpdf ' M_.fname '_IRF_' deblank(tit(i,:))]);
saveas(hh,[M_.fname '_IRF_' deblank(tit(i,:)) '.fig']);
end
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for j = 1:number_of_plots_to_draw
fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{$%s$}\n'],deblank(mylist(j,:)),deblank(mylistTeX(j,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_IRF_%s}\n',M_.fname,deblank(tit(i,:)));
fprintf(fidTeX,'\\caption{Impulse response functions (orthogonalized shock to $%s$).}',titTeX(i,:));
fprintf(fidTeX,'\\label{Fig:IRF:%s}\n',deblank(tit(i,:)));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
% close(hh)
else
for fig = 1:nbplt-1
if options_.relative_irf == 1
hh = figure('Name',['Relative response to orthogonalized shock' ...
' to ' tit(i,:) ' figure ' int2str(fig)]);
else
hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ...
' figure ' int2str(fig)]);
end
for plt = 1:nstar
subplot(nr,nc,plt);
plot(1:options_.irf,transpose(irfs((fig-1)*nstar+plt,:)),'-k','linewidth',1);
hold on
plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
hold off
xlim([1 options_.irf]);
title(deblank(mylist((fig-1)*nstar+plt,:)),'Interpreter','none');
end
eval(['print -depsc2 ' M_.fname '_IRF_' deblank(tit(i,:)) int2str(fig) '.eps']);
if ~exist('OCTAVE_VERSION')
eval(['print -dpdf ' M_.fname '_IRF_' deblank(tit(i,:)) int2str(fig)]);
saveas(hh,[M_.fname '_IRF_' deblank(tit(i,:)) int2str(fig) '.fig']);
end
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for j = 1:nstar
fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{$%s$}\n'],deblank(mylist((fig-1)*nstar+j,:)),deblank(mylistTeX((fig-1)*nstar+j,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_IRF_%s%s}\n',M_.fname,deblank(tit(i,:)),int2str(fig));
if options_.relative_irf
fprintf(fidTeX,['\\caption{Relative impulse response' ...
' functions (orthogonalized shock to $%s$).}'],deblank(titTeX(i,:)));
else
fprintf(fidTeX,['\\caption{Impulse response functions' ...
' (orthogonalized shock to $%s$).}'],deblank(titTeX(i,:)));
end
fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s:%s}\n',deblank(tit(i,:)),int2str(fig));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
% close(hh);
end
hh = figure('Name',['Orthogonalized shock to ' tit(i,:) ' figure ' int2str(nbplt) '.']);
m = 0;
for plt = 1:number_of_plots_to_draw-(nbplt-1)*nstar;
m = m+1;
subplot(lr,lc,m);
plot(1:options_.irf,transpose(irfs((nbplt-1)*nstar+plt,:)),'-k','linewidth',1);
hold on
plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
hold off
xlim([1 options_.irf]);
title(deblank(mylist((nbplt-1)*nstar+plt,:)),'Interpreter','none');
end
eval(['print -depsc2 ' M_.fname '_IRF_' deblank(tit(i,:)) int2str(nbplt) '.eps']);
if ~exist('OCTAVE_VERSION')
eval(['print -dpdf ' M_.fname '_IRF_' deblank(tit(i,:)) int2str(nbplt)]);
saveas(hh,[M_.fname '_IRF_' deblank(tit(i,:)) int2str(nbplt) '.fig']);
end
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for j = 1:m
fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{$%s$}\n'],deblank(mylist((nbplt-1)*nstar+j,:)),deblank(mylistTeX((nbplt-1)*nstar+j,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_IRF_%s%s}\n',M_.fname,deblank(tit(i,:)),int2str(nbplt));
if options_.relative_irf
fprintf(fidTeX,['\\caption{Relative impulse response functions' ...
' (orthogonalized shock to $%s$).}'],deblank(titTeX(i,:)));
else
fprintf(fidTeX,['\\caption{Impulse response functions' ...
' (orthogonalized shock to $%s$).}'],deblank(titTeX(i,:)));
end
fprintf(fidTeX,'\\label{Fig:IRF:%s:%s}\n',deblank(tit(i,:)),int2str(nbplt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
% close(hh);
end
end
end
iter_ = olditer;
if TeX
fprintf(fidTeX,' \n');
fprintf(fidTeX,'%% End Of TeX file. \n');
fclose(fidTeX);
end
end
end
if options_.SpectralDensity == 1
[omega,f] = UnivariateSpectralDensity(oo_.dr,var_list);
end
options_ = options_old;

BIN
mex/2007b/simulate.mexw32
View File

Binary file not shown.

2
mex/sources/simulate/Interpreter.cc
View File

@ -193,12 +193,12 @@ Interpreter::compute_block_time() /*throw(EvalException)*/
mexEvalString("drawnow;");
#endif
y[(it_+lag)*y_size+var] = Stack.top();
#ifdef DEBUGC
if(var==153)
{
mexPrintf(" FSTP y[var=%d,time=%d,lag=%d,%d]=%f\n",var,it_,lag,(it_+lag)*y_size+var,y[(it_+lag)*y_size+var]);
mexEvalString("drawnow;");
}
#ifdef DEBUGC
mexPrintf("%f\n",y[(it_+lag)*y_size+var]);
mexEvalString("drawnow;");
#endif

7
mex/sources/simulate/SparseMatrix.cc
View File

@ -601,6 +601,7 @@ void SparseMatrix::Init(int periods, int y_kmin, int y_kmax, int Size, std::map<
mexEvalString("drawnow;");
#endif
tmp_b+=u[it4->second+u_count_init*t]*y[index_vara[var+Size*(y_kmin+t)]];
//mexPrintf(" u[%d](%f)*y[%d](%f)=%f",it4->second+u_count_init*t,u[it4->second+u_count_init*t],index_vara[var+Size*(y_kmin+t)],y[index_vara[var+Size*(y_kmin+t)]],u[it4->second+u_count_init*t]*y[index_vara[var+Size*(y_kmin+t)]]);
}
}
else
@ -610,6 +611,7 @@ void SparseMatrix::Init(int periods, int y_kmin, int y_kmax, int Size, std::map<
#endif
b[eq]=it4->second+u_count_init*t;
u[b[eq]]+=tmp_b;
//mexPrintf("u[%d]=%f corr=%f\n",b[eq],u[b[eq]],tmp_b);
#ifdef PRINT_OUT
mexPrintf("=> b[%d]=%f\n", eq, u[b[eq]]);
mexEvalString("drawnow;");
@ -626,6 +628,7 @@ void SparseMatrix::Init(int periods, int y_kmin, int y_kmax, int Size, std::map<
mexPrintf("end of Init\n");
mexEvalString("drawnow;");
#endif
//mexEvalString("Init");
mxFree(temp_NZE_R);
mxFree(temp_NZE_C);
}
@ -641,7 +644,7 @@ void SparseMatrix::ShortInit(int periods, int y_kmin, int y_kmax, int Size, std:
#ifdef PRINT_OUT
mexPrintf("t=%d\n",t);
#endif
int ti_y_kmin=-min( t , y_kmin);
int ti_y_kmin=-min( t , y_kmin);
int ti_y_kmax= min( periods-(t+1), y_kmax);
it4=IM.begin();
eq=-1;
@ -681,6 +684,7 @@ void SparseMatrix::ShortInit(int periods, int y_kmin, int y_kmax, int Size, std:
#endif
b[eq]=it4->second+u_count_init*t;
u[b[eq]]+=tmp_b;
//mexPrintf("u[%d]=%f\n",b[eq],u[b[eq]]);
#ifdef PRINT_OUT
mexPrintf("=> b[%d]=%f\n", eq, u[b[eq]]);
#endif
@ -691,6 +695,7 @@ void SparseMatrix::ShortInit(int periods, int y_kmin, int y_kmax, int Size, std:
it4++;
}
}
//mexPrintf("ShortInit\n");
}

928
preprocessor/BlockTriangular.cc
View File

File diff suppressed because it is too large Load Diff

6
preprocessor/ComputingTasks.cc
View File

@ -169,7 +169,11 @@ StochSimulStatement::writeOutput(ostream &output, const string &basename) const
{
options_list.writeOutput(output);
symbol_list.writeOutput("var_list_", output);
output << "info = stoch_simul(var_list_);\n";
output << "if(options_.model_mode)\n";
output << " info = stoch_simul_sparse(var_list_);\n";
output << "else\n";
output << " info = stoch_simul(var_list_);\n";
output << "end\n";
}
ForecastStatement::ForecastStatement(const SymbolList &symbol_list_arg,

49
preprocessor/ExprNode.cc
View File

@ -164,6 +164,12 @@ NumConstNode::collectEndogenous(set<pair<int, int> > &result) const
{
}
void
NumConstNode::collectExogenous(set<pair<int, int> > &result) const
{
}
VariableNode::VariableNode(DataTree &datatree_arg, int symb_id_arg, SymbolType type_arg, int lag_arg) :
ExprNode(datatree_arg),
symb_id(symb_id_arg),
@ -473,6 +479,14 @@ VariableNode::collectEndogenous(set<pair<int, int> > &result) const
result.insert(make_pair(symb_id, lag));
}
void
VariableNode::collectExogenous(set<pair<int, int> > &result) const
{
if (type == eExogenous)
result.insert(make_pair(symb_id, lag));
}
UnaryOpNode::UnaryOpNode(DataTree &datatree_arg, UnaryOpcode op_code_arg, const NodeID arg_arg) :
ExprNode(datatree_arg),
arg(arg_arg),
@ -864,6 +878,13 @@ UnaryOpNode::collectEndogenous(set<pair<int, int> > &result) const
arg->collectEndogenous(result);
}
void
UnaryOpNode::collectExogenous(set<pair<int, int> > &result) const
{
arg->collectExogenous(result);
}
BinaryOpNode::BinaryOpNode(DataTree &datatree_arg, const NodeID arg1_arg,
BinaryOpcode op_code_arg, const NodeID arg2_arg) :
ExprNode(datatree_arg),
@ -1322,6 +1343,14 @@ BinaryOpNode::collectEndogenous(set<pair<int, int> > &result) const
arg2->collectEndogenous(result);
}
void
BinaryOpNode::collectExogenous(set<pair<int, int> > &result) const
{
arg1->collectExogenous(result);
arg2->collectExogenous(result);
}
TrinaryOpNode::TrinaryOpNode(DataTree &datatree_arg, const NodeID arg1_arg,
TrinaryOpcode op_code_arg, const NodeID arg2_arg, const NodeID arg3_arg) :
ExprNode(datatree_arg),
@ -1334,7 +1363,7 @@ TrinaryOpNode::TrinaryOpNode(DataTree &datatree_arg, const NodeID arg1_arg,
// Non-null derivatives are the union of those of the arguments
// Compute set union of arg{1,2,3}->non_null_derivatives
set<int> non_null_derivatives_tmp;
set<int> non_null_derivatives_tmp;
set_union(arg1->non_null_derivatives.begin(),
arg1->non_null_derivatives.end(),
arg2->non_null_derivatives.begin(),
@ -1590,6 +1619,15 @@ TrinaryOpNode::collectEndogenous(set<pair<int, int> > &result) const
arg3->collectEndogenous(result);
}
void
TrinaryOpNode::collectExogenous(set<pair<int, int> > &result) const
{
arg1->collectExogenous(result);
arg2->collectExogenous(result);
arg3->collectExogenous(result);
}
UnknownFunctionNode::UnknownFunctionNode(DataTree &datatree_arg,
int symb_id_arg,
const vector<NodeID> &arguments_arg) :
@ -1650,6 +1688,15 @@ UnknownFunctionNode::collectEndogenous(set<pair<int, int> > &result) const
(*it)->collectEndogenous(result);
}
void
UnknownFunctionNode::collectExogenous(set<pair<int, int> > &result) const
{
for(vector<NodeID>::const_iterator it = arguments.begin();
it != arguments.end(); it++)
(*it)->collectExogenous(result);
}
double
UnknownFunctionNode::eval(const eval_context_type &eval_context) const throw (EvalException)
{

2
preprocessor/ModFile.cc
View File

@ -69,7 +69,7 @@ ModFile::checkPass()
exit(EXIT_FAILURE);
}
if (mod_file_struct.simul_present && stochastic_statement_present)
if (mod_file_struct.simul_present && stochastic_statement_present && model_tree.mode==0)
{
cerr << "ERROR: A .mod file cannot contain both a simul command and one of {stoch_simul, estimation, forecast, osr, ramsey_policy}" << endl;
exit(EXIT_FAILURE);

764
preprocessor/ModelTree.cc
View File

File diff suppressed because it is too large Load Diff

18
preprocessor/ParsingDriver.cc
View File

@ -169,7 +169,7 @@ ParsingDriver::add_model_variable(string *name, string *olag)
if (type == eUnknownFunction)
error("Symbol " + *name + " is a function name unknown to Dynare. It cannot be used inside model.");
if (type == eExogenous && lag != 0)
if (type == eExogenous && lag != 0 && (model_tree->mode != eSparseDLLMode && model_tree->mode != eSparseMode))
warning("Exogenous variable " + *name + " has lead/lag " + *olag);
if (type == eModelLocalVariable && lag != 0)
@ -179,9 +179,13 @@ ParsingDriver::add_model_variable(string *name, string *olag)
NodeID id = model_tree->AddVariable(*name, lag);
if ((type == eEndogenous) && (model_tree->mode == eSparseDLLMode || model_tree->mode == eSparseMode))
model_tree->block_triangular.fill_IM(model_tree->equation_number(), mod_file->symbol_table.getID(*name), lag);
/*if (model_tree->mode == eSparseDLLMode || model_tree->mode == eSparseMode)
{
if (type == eEndogenous)
model_tree->block_triangular.fill_IM(model_tree->equation_number(), mod_file->symbol_table.getID(*name), lag);
if (type == eExogenous)
model_tree->block_triangular.fill_IM_X(model_tree->equation_number(), mod_file->symbol_table.getID(*name), lag);
}*/
delete name;
delete olag;
return id;
@ -360,14 +364,16 @@ void
ParsingDriver::sparse_dll()
{
model_tree->mode = eSparseDLLMode;
model_tree->block_triangular.init_incidence_matrix(mod_file->symbol_table.endo_nbr);
/*model_tree->block_triangular.init_incidence_matrix(mod_file->symbol_table.endo_nbr);
model_tree->block_triangular.init_incidence_matrix_X(mod_file->symbol_table.exo_nbr);*/
}
void
ParsingDriver::sparse()
{
model_tree->mode = eSparseMode;
model_tree->block_triangular.init_incidence_matrix(mod_file->symbol_table.endo_nbr);
/*model_tree->block_triangular.init_incidence_matrix(mod_file->symbol_table.endo_nbr);
model_tree->block_triangular.init_incidence_matrix_X(mod_file->symbol_table.exo_nbr);*/
}
void

56
preprocessor/include/BlockTriangular.hh
View File

@ -36,41 +36,61 @@ struct List_IM
bool* IM;
};
//! create and manage the incidence matrix
class IncidenceMatrix //: SymbolTable
{
//friend class BlockTriangular;
public:
const SymbolTable &symbol_table;
IncidenceMatrix(const SymbolTable &symbol_table_arg);
List_IM* Build_IM(int lead_lag, SymbolType type);
List_IM* Get_IM(int lead_lag, SymbolType type) const;
bool* bGet_IM(int lead_lag, SymbolType type) const;
void fill_IM(int equation, int variable_endo, int lead_lag, SymbolType type);
void unfill_IM(int equation, int variable_endo, int lead_lag, SymbolType type);
void init_incidence_matrix();
void Free_IM() const;
List_IM* Get_First(SymbolType type) const;
void Print_IM(SymbolType type) const;
void Print_SIM(bool* IM, SymbolType type) const;
void swap_IM_c(bool *SIM, int pos1, int pos2, int pos3, simple* Index_Var_IM, simple* Index_Equ_IM, int n) const;
private:
List_IM *First_IM, *Last_IM, *First_IM_X, *Last_IM_X ;
public:
int Model_Max_Lead, Model_Max_Lag;
int Model_Max_Lead_Endo, Model_Max_Lag_Endo, Model_Max_Lead_Exo, Model_Max_Lag_Exo;
};
//! Matrix of doubles for representing jacobian
typedef map<pair<int ,int >,double> jacob_map;
//! Create the incidence matrix, computes prologue & epilogue, normalizes the model and computes the block decomposition
class BlockTriangular
{
//friend class IncidenceMatrix;
public:
BlockTriangular(const SymbolTable &symbol_table_arg);
const SymbolTable &symbol_table;
BlockTriangular(const SymbolTable &symbol_table_arg);
//BlockTriangular(const IncidenceMatrix &incidence_matrix_arg);
//const SymbolTable &symbol_table;
Blocks blocks;
Normalization normalization;
List_IM* Build_IM(int lead_lag);
List_IM* Get_IM(int lead_lag);
bool* bGet_IM(int lead_lag) const;
void fill_IM(int equation, int variable_endo, int lead_lag);
void unfill_IM(int equation, int variable_endo, int lead_lag);
void init_incidence_matrix(int nb_endo);
void Print_IM(int n) const;
void Free_IM(List_IM* First_IM) const;
void Print_SIM(bool* IM, int n) const;
IncidenceMatrix incidencematrix;
void Normalize_and_BlockDecompose_Static_0_Model(const jacob_map &j_m);
bool Normalize_and_BlockDecompose(bool* IM, Model_Block* ModelBlock, int n, int* prologue, int* epilogue, simple* Index_Var_IM, simple* Index_Equ_IM, bool Do_Normalization, bool mixing, bool* IM_0 , jacob_map j_m);
void Prologue_Epilogue(bool* IM, int* prologue, int* epilogue, int n, simple* Index_Var_IM, simple* Index_Equ_IM, bool* IM0);
void swap_IM_c(bool *SIM, int pos1, int pos2, int pos3, simple* Index_Var_IM, simple* Index_Equ_IM, int n);
void Allocate_Block(int size, int *count_Equ, int *count_Block, BlockType type, Model_Block * ModelBlock);
void Free_Block(Model_Block* ModelBlock) const;
List_IM *First_IM ;
List_IM *Last_IM ;
simple *Index_Equ_IM;
simple *Index_Var_IM;
int prologue, epilogue;
int Model_Max_Lead, Model_Max_Lag, periods;
bool bt_verbose;
int endo_nbr;
//int endo_nbr, exo_nbr;
Model_Block* ModelBlock;
int periods;
inline static std::string BlockType0(int type)
{
switch (type)
@ -98,14 +118,14 @@ public:
{
case EVALUATE_FORWARD:
case EVALUATE_FORWARD_R:
return ("EVALUATE FORWARD ");
return ("EVALUATE FORWARD ");
break;
case EVALUATE_BACKWARD:
case EVALUATE_BACKWARD_R:
return ("EVALUATE BACKWARD ");
break;
case SOLVE_FORWARD_SIMPLE:
return ("SOLVE FORWARD SIMPLE ");
return ("SOLVE FORWARD SIMPLE ");
break;
case SOLVE_BACKWARD_SIMPLE:
return ("SOLVE BACKWARD SIMPLE ");
@ -114,7 +134,7 @@ public:
return ("SOLVE TWO BOUNDARIES SIMPLE ");
break;
case SOLVE_FORWARD_COMPLETE:
return ("SOLVE FORWARD COMPLETE ");
return ("SOLVE FORWARD COMPLETE ");
break;
case SOLVE_BACKWARD_COMPLETE:
return ("SOLVE BACKWARD COMPLETE ");

20
preprocessor/include/ExprNode.hh
View File

@ -143,6 +143,7 @@ public:
/*! Endogenous are stored as integer pairs of the form (symb_id, lag)
They are added to the set given in argument */
virtual void collectEndogenous(set<pair<int, int> > &result) const = 0;
virtual void collectExogenous(set<pair<int, int> > &result) const = 0;
virtual void computeTemporaryTerms(map<NodeID, int> &reference_count,
temporary_terms_type &temporary_terms,
map<NodeID, int> &first_occurence,
@ -179,6 +180,7 @@ public:
NumConstNode(DataTree &datatree_arg, int id_arg);
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
};
@ -198,6 +200,7 @@ public:
VariableNode(DataTree &datatree_arg, int symb_id_arg, SymbolType type_arg, int lag_arg);
virtual void writeOutput(ostream &output, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms = temporary_terms_type()) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
};
@ -223,6 +226,7 @@ public:
Model_Block *ModelBlock,
map_idx_type &map_idx) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
static double eval_opcode(UnaryOpcode op_code, double v) throw (EvalException);
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
@ -250,6 +254,7 @@ public:
Model_Block *ModelBlock,
map_idx_type &map_idx) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
static double eval_opcode(double v1, BinaryOpcode op_code, double v2) throw (EvalException);
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
@ -282,6 +287,7 @@ public:
Model_Block *ModelBlock,
map_idx_type &map_idx) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
static double eval_opcode(double v1, TrinaryOpcode op_code, double v2, double v3) throw (EvalException);
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
@ -307,6 +313,7 @@ public:
Model_Block *ModelBlock,
map_idx_type &map_idx) const;
virtual void collectEndogenous(set<pair<int, int> > &result) const;
virtual void collectExogenous(set<pair<int, int> > &result) const;
virtual double eval(const eval_context_type &eval_context) const throw (EvalException);
virtual void compile(ofstream &CompileCode, bool lhs_rhs, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, map_idx_type map_idx) const;
};
@ -314,21 +321,22 @@ public:
//! For one lead/lag of one block, stores mapping of information between original model and block-decomposed model
struct IM_compact
{
int size, u_init, u_finish, nb_endo;
int *u, *us, *Var, *Equ, *Var_Index, *Equ_Index, *Var_dyn_Index;
int size, u_init, u_finish, nb_endo, size_exo;
int *u, *us, *Var, *Equ, *Var_Index, *Equ_Index, *Exogenous, *Exogenous_Index, *Equ_X, *Equ_X_Index;
};
//! One block of the model
struct Block
{
int Size, Sized;
int Size, Sized, nb_exo, nb_exo_det;
BlockType Type;
BlockSimulationType Simulation_Type;
int Max_Lead, Max_Lag, Nb_Lead_Lag_Endo;
int Max_Lag_Endo, Max_Lead_Endo;
int Max_Lag_Exo, Max_Lead_Exo;
bool is_linear;
int *Equation, *Own_Derivative;
int *Variable, *Variable_Sorted, *dVariable;
int *variable_dyn_index, *variable_dyn_leadlag;
int *Variable, *Exogenous;
temporary_terms_type *Temporary_terms;
IM_compact *IM_lead_lag;
int Code_Start, Code_Length;
@ -339,7 +347,7 @@ struct Model_Block
{
int Size, Periods;
Block* Block_List;
int *in_Block_Equ, *in_Block_Var, *in_Equ_of_Block, *in_Var_of_Block;
//int *in_Block_Equ, *in_Block_Var, *in_Equ_of_Block, *in_Var_of_Block;
};
#endif

5
preprocessor/include/ModelTree.hh
View File

@ -26,6 +26,7 @@ using namespace std;
#include <vector>
#include <map>
#include <ostream>
#include <algorithm>
#include "SymbolTable.hh"
#include "NumericalConstants.hh"
@ -82,12 +83,14 @@ private:
//! Computes derivatives of ModelTree
void derive(int order);
//! Write derivative of an equation w.r. to a variable
void writeDerivative(ostream &output, int eq, int symb_id, int lag, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms) const;
void writeDerivative(ostream &output, int eq, int symb_id, int lag, ExprNodeOutputType output_type, const temporary_terms_type &temporary_terms, SymbolType type) const;
//! Write derivative code of an equation w.r. to a variable
void compileDerivative(ofstream &code_file, int eq, int symb_id, int lag, ExprNodeOutputType output_type, map_idx_type map_idx) const;
//! Computes temporary terms
void computeTemporaryTerms(int order);
void computeTemporaryTermsOrdered(int order, Model_Block *ModelBlock);
//! Build The incidence matrix form the modeltree
void BuildIncidenceMatrix();
//! Writes temporary terms
void writeTemporaryTerms(ostream &output, ExprNodeOutputType output_type) const;
//! Writes model local variables

8
tests/ferhat/fs2000.mod
View File

@ -48,7 +48,7 @@ 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;
vv = 0.2*ww+0.5*vv(-1)+1;
vv = 0.2*ww+0.5*vv(-1)+1+c(-1)+e_a;
ww = 0.1*vv+0.5*ww(-1)+2;
/* A lt=
0.5*vv-0.2*ww = 1
@ -84,11 +84,11 @@ vv = 0;
ww = 0;
end;
/*shocks;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
end;
*/
options_.solve_tolf=1e-10;
options_.maxit_=100;
steady;
@ -102,6 +102,8 @@ end;
simul(periods=200, method=lu);
stoch_simul(periods=200,order=1);
rplot y;
rplot k;
rplot c;

2
tests/ferhat/ireland.mod
View File

@ -89,7 +89,7 @@ end;
options_.maxit_=20;
model_info;
simul(periods=200, method=/*LU*/GMRES/*bicgstab*/);
simul(periods=2000, method=/*LU*/GMRES/*bicgstab*/);
rplot y;
rplot k;

35
tests/ferhat/ls2003.mod
View File

@ -1,4 +1,4 @@
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs vv ww;
varexo e_R e_q e_ys e_pies e_A;
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
@ -31,9 +31,22 @@ A = rho_A*A(-1)+e_A;
y_obs = y-y(-1)+A;
pie_obs = 4*pie;
R_obs = 4*R;
vv = 0.2*ww+0.5*vv(-1)+1;
ww = 0.1*vv+0.5*ww(-1)+2;
/* A lt=
0.5*vv-0.2*ww = 1
-0.1*vv+0.5*ww = 2
[ 0.5 -0.2][vv] [1]
=
[-0.1 0.5][ww] [2]
det = 0.25-0.02 = 0.23
[vv] [0.5 0.2] [1] [0.9] [3.91304]
= 1/0.23* = 1/0.23* =
[ww] [0.1 0.5] [2] [1.1] [4.7826]
*/
end;
/*shocks;
shocks;
var e_R = 1.25^2;
var e_q = 2.5^2;
var e_A = 1.89;
@ -41,7 +54,7 @@ var e_ys = 1.89;
var e_pies = 1.89;
end;
varobs y_obs R_obs pie_obs dq de;
/*varobs y_obs R_obs pie_obs dq de;
estimated_params;
psi1 , gamma_pdf,1.5,0.5;
@ -63,12 +76,13 @@ stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
end;
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=10,prefilter=1,mh_jscale=0.5,mh_replic=0);
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=10,prefilter=1,mh_jscale=0.5,mh_replic=0,nograph);
*/
options_.maxit_=100;
steady;
//model_info;
model_info;
check;
shocks;
@ -77,6 +91,9 @@ periods 1;
values 0.5;
end;
//simul(periods=200,method=bicgstab);
//rplot A;
//rplot pie;
simul(periods=200,method=bicgstab);
rplot A;
rplot pie;
stoch_simul(periods=200,order=1);

2
tests/ferhat/ramst.mod
View File

@ -32,7 +32,7 @@ periods 1;
values 1.002;
end;
simul(periods=200, METHOD=GmRes);
simul(periods=200, METHOD=LU);
rplot c;
rplot k;