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v4.1: Removed globals. Removed call to generalized Cholesky. Added handling of datasets with missing observations (NaN), not yet ready. Changed the covariance matrix of the jumping distribution when the posterior mode is not computed (or used). Changed call to dynare_estimation. Removed pindx in dynare_estimation. + Cosmetic changes. 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2135 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2008-10-04 21:22:08 +00:00
parent 7a93a8cda8
commit f51ba0bd93
4 changed files with 148 additions and 154 deletions
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224
matlab/dynare_estimation.m
View File

@ -1,4 +1,4 @@
function dynare_estimation(var_list_) function dynare_estimation(var_list_,dname)
% function dynare_estimation(var_list_) % function dynare_estimation(var_list_)
% runs the estimation of the model % runs the estimation of the model
% %
@ -30,12 +30,9 @@ function dynare_estimation(var_list_)
global M_ options_ oo_ estim_params_ bayestopt_ global M_ options_ oo_ estim_params_ bayestopt_
%% Build var_list_
var_list_ = check_list_of_variables(options_, M_, var_list_); var_list_ = check_list_of_variables(options_, M_, var_list_);
%if isempty(var_list_) options_.varlist = var_list_;
% return
%else
options_.varlist = var_list_;
%end
options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1); options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
for i = 1:size(M_.endo_names,1) for i = 1:size(M_.endo_names,1)
@ -45,24 +42,28 @@ for i = 1:size(M_.endo_names,1)
end end
end end
%% Decide if a DSGE or DSGE-VAR has to be estimated.
if ~isempty(strmatch('dsge_prior_weight',M_.param_names)) if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
options_.bvar_dsge = 1; options_.bvar_dsge = 1;
end end
%% Set the order of approximation to one (if needed).
if options_.order > 1 if options_.order > 1
options_.order = 1; options_.order = 1;
end end
if (~isempty(options_.unit_root_vars) || options_.diffuse_filter == 1) %% Set options_.lik_init equal to 3 if diffuse filter is used.
if options_.lik_init == 1 if (options_.diffuse_filter==1) && (options_.lik_init==1)
options_.lik_init = 3; options_.lik_init = 3;
end
end end
%% If the data are prefiltered then there must not be constants in the
%% measurement equation of the DSGE model or in the DSGE-VAR model.
if options_.prefilter == 1 if options_.prefilter == 1
options_.noconstant = 1; options_.noconstant = 1;
end end
%% Set options related to filtered variables.
if options_.filtered_vars ~= 0 & isempty(options_.filter_step_ahead), if options_.filtered_vars ~= 0 & isempty(options_.filter_step_ahead),
options_.filter_step_ahead = 1; options_.filter_step_ahead = 1;
end end
@ -73,32 +74,39 @@ if options_.filter_step_ahead ~= 0
options_.nk = max(options_.filter_step_ahead); options_.nk = max(options_.filter_step_ahead);
end end
%% Add something to the parser ++> %% Set the name of the directory where (intermediary) results will be saved.
% The user should be able to choose another name if nargin>1
% for the directory... M_.dname = dname;
M_.dname = M_.fname; else
M_.dname = M_.fname;
end
%% Set the names of the priors.
pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2']; %% Set the number of observed variables.
n_varobs = size(options_.varobs,1); n_varobs = size(options_.varobs,1);
%% Set priors over the estimated parameters.
if ~isempty(estim_params_) if ~isempty(estim_params_)
[xparam1,estim_params_,bayestopt_,lb,ub] = set_prior(estim_params_); [xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
if any(bayestopt_.pshape > 0) if any(bayestopt_.pshape > 0)
% Plot prior densities.
if options_.mode_compute if options_.mode_compute
plot_priors plot_priors(bayestopt_,M_,options_)
end end
% set prior bounds and check initial value of the parameters % Set prior bounds
bounds = prior_bounds(bayestopt_); bounds = prior_bounds(bayestopt_);
bounds(:,1)=max(bounds(:,1),lb); bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub); bounds(:,2)=min(bounds(:,2),ub);
else else
options_.mh_replic = 0; % No priors are declared so Dynare will estimate the model by
% maximum likelihood with inequality constraints for the parameters.
options_.mh_replic = 0;% No metropolis.
bounds(:,1) = lb; bounds(:,1) = lb;
bounds(:,2) = ub; bounds(:,2) = ub;
end end
% Test if initial values of the estimated parameters are all between
% the prior lower and upper bounds.
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2)) if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1)) find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2)) find(xparam1 > bounds(:,2))
@ -108,7 +116,7 @@ if ~isempty(estim_params_)
ub = bounds(:,2); ub = bounds(:,2);
bayestopt_.lb = lb; bayestopt_.lb = lb;
bayestopt_.ub = ub; bayestopt_.ub = ub;
else else% If estim_params_ is empty...
xparam1 = []; xparam1 = [];
bayestopt_.lb = []; bayestopt_.lb = [];
bayestopt_.ub = []; bayestopt_.ub = [];
@ -117,66 +125,65 @@ else
bayestopt_.p1 = []; bayestopt_.p1 = [];
bayestopt_.p2 = []; bayestopt_.p2 = [];
bayestopt_.p3 = []; bayestopt_.p3 = [];
bayestopt_.p4 = [];
estim_params_.nvx = 0;
estim_params_.nvn = 0; estim_params_.nvn = 0;
estim_params_.ncx = 0; estim_params_.ncx = 0;
estim_params_.ncn = 0; estim_params_.ncn = 0;
estim_params_.np = 0; estim_params_.np = 0;
end end
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
ncx = estim_params_.ncx;
ncn = estim_params_.ncn;
np = estim_params_.np ;
nx = nvx+nvn+ncx+ncn+np;
if ~isfield(options_,'trend_coeffs') %% Get the number of parameters to be estimated.
bayestopt_.with_trend = 0; nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
else nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
bayestopt_.with_trend = 1; ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
bayestopt_.trend_coeff = {}; ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
trend_coeffs = options_.trend_coeffs; np = estim_params_.np ; % Number of deep parameters.
nt = length(trend_coeffs); nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
for i=1:n_varobs
if i > length(trend_coeffs) %% Is there a linear trend in the measurement equation?
bayestopt_.trend_coeff{i} = '0'; if ~isfield(options_,'trend_coeffs') % No!
else bayestopt_.with_trend = 0;
bayestopt_.trend_coeff{i} = trend_coeffs{i}; else% Yes!
bayestopt_.with_trend = 1;
bayestopt_.trend_coeff = {};
trend_coeffs = options_.trend_coeffs;
nt = length(trend_coeffs);
for i=1:n_varobs
if i > length(trend_coeffs)
bayestopt_.trend_coeff{i} = '0';
else
bayestopt_.trend_coeff{i} = trend_coeffs{i};
end
end end
end
end end
bayestopt_.penalty = 1e8; % penalty %% Set the "size" of penalty.
bayestopt_.penalty = 1e8;
%% Get informations about the variables of the model.
dr = set_state_space([],M_); dr = set_state_space([],M_);
nstatic = dr.nstatic; nstatic = dr.nstatic; % Number of static variables.
npred = dr.npred; npred = dr.npred; % Number of predetermined variables.
nspred = dr.nspred; nspred = dr.nspred; % Number of predetermined variables in the state equation.
%% Test if observed variables are declared.
if isempty(options_.varobs) if isempty(options_.varobs)
error('ESTIMATION: VAROBS is missing') error('ESTIMATION: VAROBS is missing')
end end
%% Setting resticted state space (observed + predetermined variables) %% Setting resticted state space (observed + predetermined variables)
k = []; k = [];
k1 = []; k1 = [];
for i=1:n_varobs for i=1:n_varobs
k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')]; k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')]; k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
end end
% union of observed and state variables % Define union of observed and state variables
k2 = union(k',[dr.nstatic+1:dr.nstatic+dr.npred]'); k2 = union(k',[dr.nstatic+1:dr.nstatic+dr.npred]');
% Set restrict_state to postion of observed + state variables in expanded state vector.
% set restrict_state to postion of observed + state variables
% in expanded state vector
bayestopt_.restrict_var_list = k2; bayestopt_.restrict_var_list = k2;
% set mf1 to positions of observed variables in restricted state vector % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
% for likelihood computation
[junk,bayestopt_.mf1] = ismember(k,k2); [junk,bayestopt_.mf1] = ismember(k,k2);
% set mf2 to positions of observed variables in expanded state vector % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
% for filtering and smoothing
bayestopt_.mf2 = k; bayestopt_.mf2 = k;
bayestopt_.mfys = k1; bayestopt_.mfys = k1;
@ -188,8 +195,7 @@ k = find(aux(:,2) > npred);
aux(k,2) = aux(k,2) + sum(k2 > nstatic+npred); aux(k,2) = aux(k,2) + sum(k2 > nstatic+npred);
bayestopt_.restrict_aux = aux; bayestopt_.restrict_aux = aux;
%% Initialization with unit-root variables.
%% Initialization with unit-root variables
if ~isempty(options_.unit_root_vars) if ~isempty(options_.unit_root_vars)
n_ur = size(options_.unit_root_vars,1); n_ur = size(options_.unit_root_vars,1);
i_ur = zeros(n_ur,1); i_ur = zeros(n_ur,1);
@ -219,46 +225,53 @@ if ~isempty(options_.unit_root_vars)
options_.lik_init = 3; options_.lik_init = 3;
end % if ~isempty(options_.unit_root_vars) end % if ~isempty(options_.unit_root_vars)
%% Test if the data file is declared.
if isempty(options_.datafile) if isempty(options_.datafile)
error('ESTIMATION: datafile option is missing') error('ESTIMATION: datafile option is missing')
end end
%% If jscale isn't specified for an estimated parameter, use %% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
%% global option options_.jscale, set to 0.2, by default
k = find(isnan(bayestopt_.jscale)); k = find(isnan(bayestopt_.jscale));
bayestopt_.jscale(k) = options_.mh_jscale; bayestopt_.jscale(k) = options_.mh_jscale;
%% Read and demean data %% Load and transform data.
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range); rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
% Set the number of observations (nobs) and build a subsample between first_obs and nobs.
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1); options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs; gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:); rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1 & ~options_.logdata % Take the log of the variables if needed
rawdata = log(rawdata); if options_.loglinear % If the model is log-linearized...
if ~options_.logdata % and if the data are not in logs, then...
rawdata = log(rawdata);
end
end end
if options_.prefilter == 1 % Test if the observations are real numbers.
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
else
data = transpose(rawdata);
end
if ~isreal(rawdata) if ~isreal(rawdata)
error(['There are complex values in the data. Probably a wrong' ... error('There are complex values in the data! Probably a wrong transformation')
' transformation'])
end end
% Test for missing observations.
options_.missing_data = any(any(isnan(rawdata)));
% Prefilter the data if needed.
if options_.prefilter == 1
if options_.missing_data
bayestopt_.mean_varobs = zeros(1,n_varobs);
[rdx,cdx] = find(~isnan(rawdata));
for variable=1:n_varobs
tdx = find(cdx==variable);
bayestopt_.mean_varobs(variable) = mean(rawdata(rdx(tdx)),variable);
end
else
bayestopt_.mean_varobs = mean(rawdata,1);
rawdata = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
end
end
% Transpose the dataset array.
data = transpose(rawdata);
% set options for recursive forecast if necessary %% Set various options.
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1); options_ = set_default_option(options_,'mh_nblck',2);
if options_.nobs(1) == options_.nobs(end) % no recursive estimation options_ = set_default_option(options_,'nodiagnostic',0);
options_ = set_default_option(options_,'mh_nblck',2);
options_ = set_default_option(options_,'nodiagnostic',0);
else
options_ = set_default_option(options_,'mh_nblck',1);
options_ = set_default_option(options_,'nodiagnostic',1);
end
% load mode file is necessary % load mode file is necessary
if length(options_.mode_file) > 0 && options_.posterior_mode_estimation if length(options_.mode_file) > 0 && options_.posterior_mode_estimation
@ -269,14 +282,13 @@ end
if ~isempty(estim_params_) if ~isempty(estim_params_)
set_parameters(xparam1); set_parameters(xparam1);
end end
if options_.steadystate_flag% if the _steadystate.m file is provided. if options_.steadystate_flag% if the *_steadystate.m file is provided.
[oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]); [oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
else% if the steady state file is not provided. else% if the steady state file is not provided.
[dd,info] = resol(oo_.steady_state,0); [dd,info] = resol(oo_.steady_state,0);
oo_.steady_state = dd.ys; clear('dd'); oo_.steady_state = dd.ys; clear('dd');
end end
if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9) if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
disp('No constant.')
options_.noconstant = 1; options_.noconstant = 1;
else else
options_.noconstant = 0; options_.noconstant = 0;
@ -323,7 +335,7 @@ if options_.mode_compute == 0 & length(options_.mode_file) == 0
eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']); eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
end end
end end
return; return;
end end
%% Estimation of the posterior mode or likelihood mode %% Estimation of the posterior mode or likelihood mode
@ -346,16 +358,8 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ... [xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend); fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend);
end end
elseif options_.mode_compute == 2 elseif options_.mode_compute == 2
% asamin('set','maximum_cost_repeat',0); error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
% $$$ if ~options_.bvar_dsge
% $$$ [fval,xparam1,grad,hessian_asamin,exitflag] = ...
% $$$ asamin('minimize','DsgeLikelihood',xparam1,lb,ub,-ones(size(xparam1)),gend,data);
% $$$ else
% $$$ [fval,xparam1,grad,hessian_asamin,exitflag] = ...
% $$$ asamin('minimize','DsgeVarLikelihood',xparam1,lb,ub,-ones(size(xparam1)),gend);
% $$$ end
error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
elseif options_.mode_compute == 3 elseif options_.mode_compute == 3
optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6); optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
if isfield(options_,'optim_opt') if isfield(options_,'optim_opt')
@ -499,14 +503,11 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
hh = reshape(hessian('DsgeVarLikelihood',xparam1,gend),nx,nx); hh = reshape(hessian('DsgeVarLikelihood',xparam1,gend),nx,nx);
end end
save([M_.fname '_mode.mat'],'xparam1','hh','fval'); save([M_.fname '_mode.mat'],'xparam1','hh','fval');
%eval(['save ' M_.fname '_mode xparam1 hh fval;']);
else else
save([M_.fname '_mode.mat'],'xparam1','hh','fval'); save([M_.fname '_mode.mat'],'xparam1','hh','fval');
%eval(['save ' M_.fname '_mode xparam1 hh fval;']);
end end
end end
save([M_.fname '_mode.mat'],'xparam1','hh'); save([M_.fname '_mode.mat'],'xparam1','hh');
%eval(['save ' M_.fname '_mode xparam1 hh;']);
end end
if options_.mode_check == 1 & options_.posterior_mode_estimation if options_.mode_check == 1 & options_.posterior_mode_estimation
@ -514,13 +515,13 @@ if options_.mode_check == 1 & options_.posterior_mode_estimation
end end
if options_.posterior_mode_estimation if options_.posterior_mode_estimation
hh = generalized_cholesky(hh); %hh = generalized_cholesky(hh);
invhess = inv(hh); invhess = inv(hh);
stdh = sqrt(diag(invhess)); stdh = sqrt(diag(invhess));
else else
variances = bayestopt_.pstdev.^2; variances = bayestopt_.pstdev.^2;
% invhess = 0.001*diag(variances); invhess = 0.01*diag(variances);
invhess = 0.01*eye(length(variances)); %invhess = 0.01*eye(length(variances));
end end
@ -932,11 +933,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
if options_.mh_replic if options_.mh_replic
[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_); [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
end end
%%
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_); oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_);
%% Results are saved (in case of an anormal exit from dynare or matlab)...
%%save([M_.fname '_results.mat'],'oo_','M_');
%%
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_); oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
metropolis_draw(1); metropolis_draw(1);
if options_.bayesian_irf if options_.bayesian_irf
@ -948,11 +945,8 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
prior_posterior_statistics('posterior',data,gend); prior_posterior_statistics('posterior',data,gend);
end end
xparam = get_posterior_parameters('mean'); xparam = get_posterior_parameters('mean');
set_all_parameters(xparam); set_all_parameters(xparam);
% return
end end
if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ... if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...

44
matlab/generalized_cholesky.m
View File

@ -28,39 +28,39 @@ norm_A = max(transpose(sum(abs(A))));
gamm = max(abs(diag(A))); gamm = max(abs(diag(A)));
delta = max([eps*norm_A;eps]); delta = max([eps*norm_A;eps]);
for j = 1:n; for j = 1:n
theta_j = 0; theta_j = 0;
for i=1:n; for i=1:n
somme = 0; somme = 0;
for k=1:i-1; for k=1:i-1
somme = somme + R(k,i)*R(k,j); somme = somme + R(k,i)*R(k,j);
end; end
R(i,j) = (A(i,j) - somme)/R(i,i); R(i,j) = (A(i,j) - somme)/R(i,i);
if (A(i,j) -somme) > theta_j; if (A(i,j) -somme) > theta_j
theta_j = A(i,j) - somme; theta_j = A(i,j) - somme;
end; end
if i > j; if i > j
R(i,j) = 0; R(i,j) = 0;
end; end
end; end
somme = 0; somme = 0;
for k=1:j-1; for k=1:j-1
somme = somme + R(k,j)^2; somme = somme + R(k,j)^2;
end; end
phi_j = A(j,j) - somme; phi_j = A(j,j) - somme;
if j+1 <= n; if j+1 <= n
xi_j = max(abs(A((j+1):n,j))); xi_j = max(abs(A((j+1):n,j)));
else; else
xi_j = abs(A(n,j)); xi_j = abs(A(n,j));
end; end
beta_j = sqrt(max([gamm ; (xi_j/n) ; eps])); beta_j = sqrt(max([gamm ; (xi_j/n) ; eps]));
if all(delta >= [abs(phi_j);((theta_j^2)/(beta_j^2))]); if all(delta >= [abs(phi_j);((theta_j^2)/(beta_j^2))])
E(j,j) = delta - phi_j; E(j,j) = delta - phi_j;
elseif all(abs(phi_j) >= [((delta^2)/(beta_j^2));delta]); elseif all(abs(phi_j) >= [((delta^2)/(beta_j^2));delta])
E(j,j) = abs(phi_j) - phi_j; E(j,j) = abs(phi_j) - phi_j;
elseif all(((theta_j^2)/(beta_j^2)) >= [delta;abs(phi_j)]); elseif all(((theta_j^2)/(beta_j^2)) >= [delta;abs(phi_j)])
E(j,j) = ((theta_j^2)/(beta_j^2)) - phi_j; E(j,j) = ((theta_j^2)/(beta_j^2)) - phi_j;
end; end
R(j,j) = sqrt(A(j,j) - somme + E(j,j)); R(j,j) = sqrt(A(j,j) - somme + E(j,j));
end; end
AA = transpose(R)*R; AA = transpose(R)*R;

13
matlab/plot_priors.m
View File

@ -1,16 +1,17 @@
function plot_priors function plot_priors(bayestopt_,M_,options_)
% function plot_priors % function plot_priors
% plots prior density % plots prior density
% %
% INPUTS % INPUTS
% none % o bayestopt_ [structure]
% o M_ [structure]
% o options_ [structure]
% %
% OUTPUTS % OUTPUTS
% none % None
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% none % None
% Copyright (C) 2004-2008 Dynare Team % Copyright (C) 2004-2008 Dynare Team
% %
@ -29,8 +30,6 @@ function plot_priors
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global bayestopt_ M_ options_
TeX = options_.TeX; TeX = options_.TeX;
figurename = 'Priors'; figurename = 'Priors';

21
matlab/set_prior.m
View File

@ -1,19 +1,22 @@
function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_) function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_)
% function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_) % function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)
% sets prior distributions % sets prior distributions
% %
% INPUTS % INPUTS
% estim_params_: structure characterizing parameters to be estimated % o estim_params_ [structure] characterizing parameters to be estimated.
% o M_ [structure] characterizing the model.
% o options_ [structure]
% %
% OUTPUTS % OUTPUTS
% xparam1: vector of parameters to be estimated (initial values) % o xparam1 [double] vector of parameters to be estimated (initial values)
% estim_params_: structure characterizing parameters to be estimated % o estim_params_ [structure] characterizing parameters to be estimated
% bayestopt_: structure characterizing priors % o bayestopt_ [structure] characterizing priors
% lb: lower bound % o lb [double] vector of lower bounds for the estimated parameters.
% ub: upper bound % o ub [double] vector of upper bounds for the estimated parameters.
% o M_ [structure] characterizing the model.
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% none % None
% Copyright (C) 2003-2008 Dynare Team % Copyright (C) 2003-2008 Dynare Team
% %
@ -31,8 +34,6 @@ function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)
% %
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_
nvx = size(estim_params_.var_exo,1); nvx = size(estim_params_.var_exo,1);
nvn = size(estim_params_.var_endo,1); nvn = size(estim_params_.var_endo,1);