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Removed globals from set_all_parameters routine.

time-shift
Stéphane Adjemian (Charybdis) 2012-06-07 14:15:59 +02:00
parent 9ce6eeebc6
commit 0da05ae29d
10 changed files with 67 additions and 48 deletions
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2
matlab/DsgeSmoother.m
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@ -62,7 +62,7 @@ decomp = [];
nobs = size(options_.varobs,1);
smpl = size(Y,2);
set_all_parameters(xparam1);
M_ = set_all_parameters(xparam1,estim_params_,M_);
%------------------------------------------------------------------------------
% 2. call model setup & reduction program

2
matlab/PosteriorFilterSmootherAndForecast.m
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@ -135,7 +135,7 @@ end
for b=1:B
%deep = GetOneDraw(NumberOfDraws,FirstMhFile,LastMhFile,FirstLine,MAX_nruns,DirectoryName);
[deep, logpo] = GetOneDraw(type);
set_all_parameters(deep);
M_ = set_all_parameters(deep,estim_params_,M_);
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = ...
DsgeSmoother(deep,gend,Y,data_index);

2
matlab/dynare_estimation_1.m
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@ -890,7 +890,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
prior_posterior_statistics('posterior',dataset_);
end
xparam = get_posterior_parameters('mean');
set_all_parameters(xparam);
M_ = set_all_parameters(xparam,estim_params_,M_);
end
end

2
matlab/get_prior_info.m
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@ -108,7 +108,7 @@ end
M_.dname = M_.fname;
if info==1% Prior simulations (BK).
results = prior_sampler(0,M_,bayestopt_,options_,oo_);
results = prior_sampler(0,M_,bayestopt_,options_,oo_,estim_params_);
% Display prior mass info
disp(['Prior mass = ' num2str(results.prior.mass)])
disp(['BK indeterminacy share = ' num2str(results.bk.indeterminacy_share)])

4
matlab/gsa/filt_mc_.m
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@ -112,12 +112,12 @@ else
end
if ~loadSA,
if exist('xparam1','var')
set_all_parameters(xparam1);
M_ = set_all_parameters(xparam1,estim_params_,M_);
steady_(M_,options_,oo_);
ys_mode=oo_.steady_state;
end
if exist('xparam1_mean','var')
set_all_parameters(xparam1_mean);
M_ = set_all_parameters(xparam1_mean,estim_params_,M_);
steady_(M_,options_,oo_);
ys_mean=oo_.steady_state;
end

2
matlab/identification_analysis.m
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@ -48,7 +48,7 @@ persistent indH indJJ indLRE
nparam=length(params);
np=length(indx);
offset=nparam-np;
set_all_parameters(params);
M_ = set_all_parameters(params,estim_params_,M_);
nlags = options_ident.ar;
useautocorr = options_ident.useautocorr;

4
matlab/prior_analysis.m
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@ -1,4 +1,4 @@
function oo_ = prior_analysis(type,arg1,arg2,arg3,options_,M_,oo_)
function oo_ = prior_analysis(type,arg1,arg2,arg3,options_,M_,oo_,estim_params_)
% Copyright (C) 2009 Dynare Team
%
% This file is part of Dynare.
@ -28,7 +28,7 @@ switch info
drsave=1;
end
load([M_.dname '/prior/definition.mat']);
prior_sampler(drsave,M_,bayestopt_,options_,oo_);
prior_sampler(drsave,M_,bayestopt_,options_,oo_,estim_params_);
clear('bayestopt_');
oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
case {4,5}

2
matlab/prior_posterior_statistics_core.m
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@ -149,7 +149,7 @@ for b=fpar:B
logpo = evaluate_posterior_kernel(deep');
end
end
set_all_parameters(deep);
M_ = set_all_parameters(deep,estim_params_,M_);
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if run_smoother

4
matlab/prior_sampler.m
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@ -1,4 +1,4 @@
function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_)
function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_,estim_params_)
% This function builds a (big) prior sample.
%
% INPUTS
@ -79,7 +79,7 @@ file_indx_number = 0;
while iteration < NumberOfSimulations
loop_indx = loop_indx+1;
params = prior_draw();
set_all_parameters(params);
M_ = set_all_parameters(params,estim_params_,M_);
[dr,INFO,M_,options_,oo_] = resol(work,M_,options_,oo_);
switch INFO(1)
case 0

91
matlab/set_all_parameters.m
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@ -1,18 +1,39 @@
function set_all_parameters(xparam1)
function M = set_all_parameters(xparam1,estim_params,M)
% function set_all_parameters(xparam1)
% Sets parameters value
%
% INPUTS
% xparam1: vector of parameters to be estimated (initial values)
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
%@info:
%! @deftypefn {Function File} {@var{M} =} dynSeries (@var{xparams1},@var{estim_params},@var{M})
%! @anchor{set_all_parameters}
%! @sp 1
%! Update parameter values (deep parameters and covariance matrices).
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item xparam1
%! N*1 vector of doubles, the values of the N estimated parameters.
%! @item estim_params
%! Dynare structure describing the estimated parameters.
%! @item M
%! Dynare structure describing the model.
%! @end table
%! @sp 1
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item M
%! Dynare structure describing the model, with updated parameters and covariances matrices.
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{DsgeSmoother}, @ref{dynare_estimation_1}, @ref{@@gsa/filt_mc_}, @ref{identification_analysis}, @ref{PosteriorFilterSmootherAndForecast}, @ref{prior_posterior_statistics_core}, @ref{prior_sampler}
%! @sp 2
%! @strong{This function calls:}
%! @sp 2
%! @end deftypefn
%@eod:
% Copyright (C) 2003-2009 Dynare Team
% Copyright (C) 2003-2009, 2012 Dynare Team
%
% This file is part of Dynare.
%
@ -29,19 +50,17 @@ function set_all_parameters(xparam1)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global estim_params_ M_
nvx = estim_params_.nvx;
ncx = estim_params_.ncx;
nvn = estim_params_.nvn;
ncn = estim_params_.ncn;
np = estim_params_.np;
Sigma_e = M_.Sigma_e;
H = M_.H;
nvx = estim_params.nvx;
ncx = estim_params.ncx;
nvn = estim_params.nvn;
ncn = estim_params.ncn;
np = estim_params.np;
Sigma_e = M.Sigma_e;
H = M.H;
% setting shocks variance
if nvx
var_exo = estim_params_.var_exo;
var_exo = estim_params.var_exo;
for i=1:nvx
k =var_exo(i,1);
Sigma_e(k,k) = xparam1(i)^2;
@ -52,7 +71,7 @@ offset = nvx;
% setting measument error variance
if nvn
var_endo = estim_params_.var_endo;
var_endo = estim_params.var_endo;
for i=1:nvn
k = var_endo(i,1);
H(k,k) = xparam1(i+offset)^2;
@ -63,16 +82,16 @@ end
offset = nvx+nvn;
% setting shocks covariances
if ~isempty(M_.Correlation_matrix)
Sigma_e = diag(sqrt(diag(Sigma_e)))*M_.Correlation_matrix*diag(sqrt(diag(Sigma_e)));
if ~isempty(M.Correlation_matrix)
Sigma_e = diag(sqrt(diag(Sigma_e)))*M.Correlation_matrix*diag(sqrt(diag(Sigma_e)));
end
if ncx
corrx = estim_params_.corrx;
corrx = estim_params.corrx;
for i=1:ncx
k1 = corrx(i,1);
k2 = corrx(i,2);
M_.Correlation_matrix(k1,k2) = xparam1(i+offset);
M_.Correlation_matrix(k2,k1) = M_.Correlation_matrix(k1,k2);
M.Correlation_matrix(k1,k2) = xparam1(i+offset);
M.Correlation_matrix(k2,k1) = M.Correlation_matrix(k1,k2);
Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e(k1,k1)*Sigma_e(k2,k2));
Sigma_e(k2,k1) = Sigma_e(k1,k2);
end
@ -82,7 +101,7 @@ end
offset = nvx+nvn+ncx;
% setting measurement error covariances
if ncn
corrn = estim_params_.corrn;
corrn = estim_params.corrn;
for i=1:ncn
k1 = corr(i,1);
k2 = corr(i,2);
@ -96,13 +115,13 @@ offset = nvx+ncx+nvn+ncn;
% setting structural parameters
%
if np
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
M.params(estim_params.param_vals(:,1)) = xparam1(offset+1:end);
end
% updating matrices in M_
if nvx
M_.Sigma_e = Sigma_e;
% updating matrices in M
if nvx || ncx
M.Sigma_e = Sigma_e;
end
if nvn
M_.H = H;
end
M.H = H;
end