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Remove full oo_ input from likelihood functions

kalman-mex
Johannes Pfeifer 2023-09-27 08:06:37 +02:00
parent 268a668f32
commit 3a115d4fcc
29 changed files with 341 additions and 298 deletions
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6
examples/Gali_2015_prior_restrictions.m
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@ -1,12 +1,12 @@
function log_prior_val=Gali_2015_prior_restrictions(M_, oo_, options_, dataset_, dataset_info);
% function prior_val=Gali_2015_prior_restrictions(M_, oo_, options_, dataset_, dataset_info);
function log_prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
% function prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
% Example of a _prior_restrictions-file automatically called during
% estimation
% It imposes a prior of the slope of the New Keynesian Phillips Curve of
% 0.03. As the slope is a composite of other parameters with independent
% priors, a separate function is required to do this.
% Copyright © 2021 Dynare Team
% Copyright © 2021-2023 Dynare Team
%
% This file is part of Dynare.
%

17
matlab/+occbin/IVF_core.m
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@ -1,6 +1,6 @@
function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
% function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
% Computes thre
function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
% [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
% Calls the solver to get the shocks explaining the data for the inversion filter
%
% Outputs:
% - filtered_errs [T by N_obs] filtered shocks
@ -11,7 +11,10 @@ function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,o
%
% Inputs
% - M_ [structure] Matlab's structure describing the model (M_).
% - oo_ [structure] Matlab's structure containing the results (oo_).
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - options_ [structure] Matlab's structure describing the current options (options_).
% - err_index [double] index of shocks with strictly positive variance in M_.exo_names
% - filtered_errs_init [T by N_obs] initial values for the shocks
@ -39,7 +42,7 @@ function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,o
[sample_length, n_obs]= size(obs);
nerrs = size(err_index,1);
if nargin<8
if nargin<11
init_val = zeros(M_.endo_nbr,1);
end
@ -83,7 +86,7 @@ for this_period=1:sample_length
opts_simul.exo_pos=err_index(inan); %err_index is predefined mapping from observables to shocks
opts_simul.endo_init = init_val_old;
opts_simul.SHOCKS = filtered_errs_init(this_period,inan);
[err_vals_out, exitflag] = dynare_solve(@occbin.match_function, filtered_errs_init(this_period,inan)', options_.occbin.solver.maxit, options_.occbin.solver.solve_tolf, options_.occbin.solver.solve_tolx, options_, obs_list, current_obs, opts_simul, M_, oo_, options_);
[err_vals_out, exitflag] = dynare_solve(@occbin.match_function, filtered_errs_init(this_period,inan)', options_.occbin.solver.maxit, options_.occbin.solver.solve_tolf, options_.occbin.solver.solve_tolx, options_, obs_list, current_obs, opts_simul, M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_);
if exitflag
filtered_errs=NaN;
@ -97,7 +100,7 @@ for this_period=1:sample_length
opts_simul.SHOCKS = err_vals_out;
[ resids(this_period,inan), ~, stateval(this_period,:), Emat(:,inan,this_period), M_] = occbin.match_function(...
err_vals_out,obs_list,current_obs,opts_simul, M_,oo_,options_);
err_vals_out,obs_list,current_obs,opts_simul, M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_);
init_val = stateval(this_period,:); %update
if max(abs(err_vals_out))>1e8
error_code(1) = 306;

25
matlab/+occbin/IVF_posterior.m
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@ -1,18 +1,23 @@
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults, atT, innov] = IVF_posterior(xparam1,...
dataset_,obs_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults, atT, innov] = IVF_posterior(xparam1,...
% dataset_,obs_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,dr, atT, innov] = IVF_posterior(xparam1,...
dataset_,dataset_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,dr, atT, innov] = IVF_posterior(xparam1,...
% dataset_,dataset_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% Computes Likelihood with inversion filter
%
% INPUTS
% - xparam1 [double] current values for the estimated parameters.
% - dataset_ [structure] dataset after transformations
% - dataset_info [structure] storing informations about the
% sample; not used but required for interface
% - DynareOptions [structure] Matlab's structure describing the current options (options_).
% - Model [structure] Matlab's structure describing the model (M_).
% - EstimatedParameters [structure] characterizing parameters to be estimated
% - BayesInfo [structure] describing the priors
% - BoundsInfo [structure] containing prior bounds
% - DynareResults [structure] Matlab's structure containing the results (oo_).
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
%
% OUTPUTS
% - fval [double] scalar, value of the likelihood or posterior kernel.
@ -25,7 +30,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOpti
% - Model [struct] Updated Model structure described in INPUTS section.
% - DynareOptions [struct] Updated DynareOptions structure described in INPUTS section.
% - BayesInfo [struct] See INPUTS section.
% - DynareResults [struct] Updated DynareResults structure described in INPUTS section.
% - dr [structure] Reduced form model.
% - atT [double] (m*T) matrix, smoothed endogenous variables (a_{t|T}) (decision-rule order)
% - innov [double] (r*T) matrix, smoothed structural shocks (r>n is the umber of shocks).
@ -76,7 +81,7 @@ err_index=DynareOptions.occbin.likelihood.IVF_shock_observable_mapping; % err_in
COVMAT1 = Model.Sigma_e(err_index,err_index);
% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state,'restrict');
[T,R,SteadyState,info,dr, Model.params] = dynare_resolve(Model,DynareOptions,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
@ -102,15 +107,15 @@ end
sample_length = size(obs,1);
filtered_errs_init = zeros(sample_length,sum(err_index));
[filtered_errs, resids, Emat, stateval, info] = occbin.IVF_core(Model,DynareResults,DynareOptions,err_index,filtered_errs_init,obs_list,obs);
[filtered_errs, resids, Emat, stateval, info] = occbin.IVF_core(Model,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,DynareOptions,err_index,filtered_errs_init,obs_list,obs);
if info(1)
fval = Inf;
exit_flag = 0;
atT=NaN(size(stateval(:,DynareResults.dr.order_var)'));
atT=NaN(size(stateval(:,dr.order_var)'));
innov=NaN(Model.exo_nbr,sample_length);
return
else
atT = stateval(:,DynareResults.dr.order_var)';
atT = stateval(:,dr.order_var)';
innov = zeros(Model.exo_nbr,sample_length);
innov(diag(Model.Sigma_e)~=0,:)=filtered_errs';
end

51
matlab/+occbin/kalman_update_algo_1.m
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@ -1,5 +1,5 @@
function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,oo_,options_,occbin_options)
% function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,oo_,options_,occbin_options)
function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options)
% function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options)
% INPUTS
% - a [N by 1] t-1's state estimate
% - a1 [N by 2] state predictions made at t-1:t
@ -18,7 +18,10 @@ function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kal
% - CC [N by 2] state space constant state transition matrix at t-1:t
% - regimes0 [structure] regime info at t-1:t
% - M_ [structure] Matlab's structure describing the model (M_).
% - oo_ [structure] Matlab's structure containing the results (oo_).
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - options_ [structure] Matlab's structure describing the current options (options_).
% - occbin_options_ [structure] Matlab's structure describing the Occbin options.
% - kalman_tol [double] tolerance for reciprocal condition number
@ -42,7 +45,7 @@ function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kal
% Philipp Pfeiffer, Marco Ratto (2021), Efficient and robust inference of models with occasionally binding
% constraints, Working Papers 2021-03, Joint Research Centre, European Commission
% Copyright © 2021-2022 Dynare Team
% Copyright © 2021-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -104,7 +107,7 @@ if ~options_.occbin.filter.use_relaxation
[a, a1, P, P1, v, alphahat, etahat, lik, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood);
else
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, base_regime,'reduced_state_space',T0,R0);
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, base_regime,'reduced_state_space',T0,R0);
regimes0(1)=base_regime;
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
@ -123,18 +126,18 @@ opts_simul.exo_pos=1:M_.exo_nbr;
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
my_order_var = oo_.dr.order_var(oo_.dr.restrict_var_list);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
my_order_var = dr.order_var(dr.restrict_var_list);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
my_order_var = oo_.dr.order_var;
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
my_order_var = dr.order_var;
end
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul.init_regime=regimes0;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
if out.error_flag
error_flag = out.error_flag;
@ -188,7 +191,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
end
regimes_(1).regimestart(end)=regimestart;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space', T0, R0);
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space', T0, R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -208,10 +211,10 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
if not(options_.occbin.filter.use_relaxation)
opts_simul.init_regime=regimes_(1);
@ -223,7 +226,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
@ -254,7 +257,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
regimes_(1).regimestart=[1 2];
end
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -273,7 +276,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
opts_simul.maxit=1;
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
@ -307,7 +310,7 @@ if ~error_flag && niter>options_.occbin.likelihood.max_number_of_iterations && ~
regimes_(1).regimestart(end)=k;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -318,10 +321,10 @@ if ~error_flag && niter>options_.occbin.likelihood.max_number_of_iterations && ~
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
% opts_simul.init_regime=regimes_(1);
if M_.occbin.constraint_nbr==1
@ -331,7 +334,7 @@ if ~error_flag && niter>options_.occbin.likelihood.max_number_of_iterations && ~
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);

59
matlab/+occbin/kalman_update_algo_3.m
View File

@ -1,5 +1,5 @@
function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,oo_,options_,occbin_options,kalman_tol,nk)
% function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,oo_,options_,occbin_options,kalman_tol,nk)
function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options,kalman_tol,nk)
% function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options,kalman_tol,nk)
%
% INPUTS
% - a [N by 1] t-1's state estimate
@ -22,7 +22,10 @@ function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat,
% - regimes0 [structure] regime info at t-1:t
% - M_ [structure] Matlab's structure describing the model (M_).
% - options_ [structure] Matlab's structure describing the current options (options_).
% - oo_ [structure] Matlab's structure containing the results (oo_).
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - occbin_options_ [structure] Matlab's structure describing the Occbin options.
% - kalman_tol [double] tolerance for reciprocal condition number
% - nk [double] number of forecasting periods
@ -52,7 +55,7 @@ function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat,
% constraints, Working Papers 2021-03, Joint Research Centre, European Commission
% Copyright © 2021 Dynare Team
% Copyright © 2021-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -104,7 +107,7 @@ if ~options_.occbin.filter.use_relaxation
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
else
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state, base_regime,myrestrict,T0,R0);
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, base_regime,myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -120,16 +123,16 @@ opts_simul.exo_pos=1:M_.exo_nbr;
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
my_order_var = oo_.dr.order_var(oo_.dr.restrict_var_list);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
my_order_var = dr.order_var(dr.restrict_var_list);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
my_order_var = oo_.dr.order_var;
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
my_order_var = dr.order_var;
end
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
return;
@ -183,7 +186,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
% % % regimestart = regimes_(1).regimestart(end-1)+round(0.5*(newstart+oldstart))-1;
regimes_(1).regimestart(end)=regimestart;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -201,15 +204,15 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.SHOCKS(1,:) = etahat(:,2)';
% if opts_simul.restrict_state_space
% tmp=zeros(M_.endo_nbr,1);
% tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
% opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
% tmp(dr.restrict_var_list,1)=alphahat(:,1);
% opts_simul.endo_init = tmp(dr.inv_order_var,1);
% else
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
% end
if not(options_.occbin.filter.use_relaxation)
@ -222,7 +225,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
return;
@ -252,7 +255,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
regimes_(1).regimestart=[1 2];
end
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -271,7 +274,7 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
opts_simul.maxit=1;
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
end
end
@ -301,7 +304,7 @@ if error_flag==0 && niter>options_.occbin.likelihood.max_number_of_iterations &&
regimes_(1).regimestart(end)=k;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
@ -309,10 +312,10 @@ if error_flag==0 && niter>options_.occbin.likelihood.max_number_of_iterations &&
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(oo_.dr.inv_order_var,1);
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
if M_.occbin.constraint_nbr==1
myregimestart = [regimes_.regimestart];
@ -321,7 +324,7 @@ if error_flag==0 && niter>options_.occbin.likelihood.max_number_of_iterations &&
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,oo_,options_);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if isequal(out.regime_history(1),regimes_(1))
error_flag=0;
break
@ -336,9 +339,9 @@ a = out.piecewise(1:nk+1,my_order_var)' - repmat(out.ys(my_order_var),1,nk+1);
T = ss.T(my_order_var,my_order_var,:);
R = ss.R(my_order_var,:,:);
C = ss.C(my_order_var,:);
TT = ss.T(oo_.dr.order_var,oo_.dr.order_var,1);
RR = ss.R(oo_.dr.order_var,:,1);
CC = ss.C(oo_.dr.order_var,1);
TT = ss.T(dr.order_var,dr.order_var,1);
RR = ss.R(dr.order_var,:,1);
CC = ss.C(dr.order_var,1);
QQ = R(:,:,2)*QQQ(:,:,3)*transpose(R(:,:,2));
P(:,:,1) = P(:,:,2);
for j=1:nk

11
matlab/+occbin/match_function.m
View File

@ -1,7 +1,7 @@
function [resids, grad, state_out, E, M_, out] = match_function(err_0, obs_list,current_obs, opts_simul,...
M_, oo_, options_)
M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_)
% function [resids, grad, stateout, E, M_, out] = match_function(err_0, obs_list,current_obs, opts_simul,...
% M_, oo_, options_)
% M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_)
% Outputs:
% - resids [double] [n_exo by 1] vector of residuals
% - grad [double] [n by n_exo] gradient (response of observables to shocks)
@ -16,7 +16,10 @@ function [resids, grad, state_out, E, M_, out] = match_function(err_0, obs_list,
% - current_obs [double] [1 by n_obs] current value of observables
% - opts_simul [structure] Structure with simulation options
% - M_ [structure] Matlab's structure describing the model (M_).
% - oo_ [structure] Matlab's structure containing the results (oo_).
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - options_ [structure] Matlab's structure describing the current options (options_).
% Original authors: Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong
@ -36,7 +39,7 @@ opts_simul.SHOCKS = err_0';
options_.occbin.simul=opts_simul;
options_.occbin.simul.full_output=1;
options_.noprint = 1;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
nobs = size(obs_list,1);
resids = zeros(nobs,1);

3
matlab/DsgeSmoother.m
View File

@ -1,4 +1,5 @@
function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0,d] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,varargin)
% [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0,d] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,varargin)
% Estimation of the smoothed variables and innovations.
%
% INPUTS
@ -123,7 +124,7 @@ else
end
if options_.occbin.smoother.status
occbin_info.status = true;
occbin_info.info= [{options_,oo_,M_} varargin];
occbin_info.info= [{options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state,M_} varargin];
else
occbin_info.status = false;
end

2
matlab/PosteriorIRF_core1.m
View File

@ -198,7 +198,7 @@ while fpar<B
end
if MAX_nirfs_dsgevar
IRUN = IRUN+1;
[~,~,~,~,~,~,~,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
[~,~,~,~,~,~,~,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight', M_.param_names));
DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));

6
matlab/cli/prior.m
View File

@ -1,5 +1,5 @@
function varargout = prior(varargin)
% varargout = prior(varargin)
% Computes various prior statistics and display them in the command window.
%
% INPUTS
@ -11,7 +11,7 @@ function varargout = prior(varargin)
% SPECIAL REQUIREMENTS
% none
% Copyright © 2015-2019 Dynare Team
% Copyright © 2015-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -139,7 +139,7 @@ if ismember('moments', varargin) % Prior simulations (2nd order moments).
% Solve model
[T,R,~,info,oo__.dr, Model.params] = dynare_resolve(Model , options_ , oo__.dr, oo__.steady_state, oo__.exo_steady_state, oo__.exo_det_steady_state,'restrict');
if ~info(1)
info=endogenous_prior_restrictions(T,R,Model , options__ , oo__);
info=endogenous_prior_restrictions(T,R,Model , options__ , oo__.dr,oo__.steady_state,oo__.exo_steady_state,oo__.exo_det_steady_state);
end
if info
skipline()

14
matlab/dsge_conditional_likelihood_1.m
View File

@ -1,5 +1,7 @@
function [fval, info, exitflag, DLIK, Hess, SteadyState, trend_coeff, Model, DynareOptions, BayesInfo, DynareResults] = ...
dsge_conditional_likelihood_1(xparam1, DynareDataset, DatasetInfo, DynareOptions, Model, EstimatedParameters, BayesInfo, BoundsInfo, DynareResults, derivatives_info)
function [fval, info, exitflag, DLIK, Hess, SteadyState, trend_coeff, Model, DynareOptions, BayesInfo, dr] = ...
dsge_conditional_likelihood_1(xparam1, DynareDataset, DatasetInfo, DynareOptions, Model, EstimatedParameters, BayesInfo, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, derivatives_info)
% [fval, info, exitflag, DLIK, Hess, SteadyState, trend_coeff, Model, DynareOptions, BayesInfo, dr] = ...
% dsge_conditional_likelihood_1(xparam1, DynareDataset, DatasetInfo, DynareOptions, Model, EstimatedParameters, BayesInfo, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, derivatives_info)
% Copyright (C) 2017-2023 Dynare Team
%
@ -62,8 +64,8 @@ end
%------------------------------------------------------------------------------
% Linearize the model around the deterministic steadystate and extract the matrices of the state equation (T and R).
[T, R, SteadyState, info,DynareResults.dr, Model.params] = ...
dynare_resolve(Model, DynareOptions, DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state, 'restrict');
[T, R, SteadyState, info,dr, Model.params] = ...
dynare_resolve(Model, DynareOptions, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, 'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
@ -86,7 +88,7 @@ if info(1)
end
% check endogenous prior restrictions
info = endogenous_prior_restrictions(T, R, Model, DynareOptions, DynareResults);
info = endogenous_prior_restrictions(T, R, Model, DynareOptions, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if info(1)
fval = Inf;
info(4)=info(2);
@ -187,7 +189,7 @@ else
end
if DynareOptions.prior_restrictions.status
tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
tmp = feval(DynareOptions.prior_restrictions.routine, Model, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, DynareOptions, DynareDataset, DatasetInfo);
fval = fval - tmp;
end

70
matlab/dsge_likelihood.m
View File

@ -1,11 +1,11 @@
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,oo_,derivatives_info)
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,bayestopt_,dr] = dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,derivatives_info)
% [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,oo_,derivatives_info)
% Evaluates the posterior kernel of a DSGE model using the specified
% kalman_algo; the resulting posterior includes the 2*pi constant of the
% likelihood function
%@info:
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{M_},@var{options_},@var{bayestopt_},@var{oo_},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{dataset_},@var{options_},@var{M_},@var{estim_params_},@var{bayestopt_},@var{oo_},@var{derivatives_flag})
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{M_},@var{options_},@var{bayestopt_},@var{dr},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{dataset_},@var{options_},@var{M_},@var{estim_params_},@var{bayestopt_},@var{oo_},@var{derivatives_flag})
%! @anchor{dsge_likelihood}
%! @sp 1
%! Evaluates the posterior kernel of a dsge model.
@ -191,30 +191,30 @@ end
%------------------------------------------------------------------------------
is_restrict_state_space = true;
if options_.occbin.likelihood.status
occbin_options = set_occbin_options(options_, M_);
occbin_options = set_occbin_options(options_);
if occbin_options.opts_simul.restrict_state_space
[T,R,SteadyState,info,oo_.dr, M_.params,TTx,RRx,CCx, T0, R0] = ...
occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state,[],'restrict');
[T,R,SteadyState,info,dr, M_.params,TTx,RRx,CCx, T0, R0] = ...
occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,[],'restrict');
else
is_restrict_state_space = false;
oldoo.restrict_var_list = oo_.dr.restrict_var_list;
oldoo.restrict_columns = oo_.dr.restrict_columns;
oo_.dr.restrict_var_list = bayestopt_.smoother_var_list;
oo_.dr.restrict_columns = bayestopt_.smoother_restrict_columns;
oldoo.restrict_var_list = dr.restrict_var_list;
oldoo.restrict_columns = dr.restrict_columns;
dr.restrict_var_list = bayestopt_.smoother_var_list;
dr.restrict_columns = bayestopt_.smoother_restrict_columns;
% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,M_,oo_.dr, M_.params,TTx,RRx,CCx, T0, R0] = ...
occbin.dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[T,R,SteadyState,info,M_,dr, M_.params,TTx,RRx,CCx, T0, R0] = ...
occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
oo_.dr.restrict_var_list = oldoo.restrict_var_list;
oo_.dr.restrict_columns = oldoo.restrict_columns;
dr.restrict_var_list = oldoo.restrict_var_list;
dr.restrict_columns = oldoo.restrict_columns;
end
occbin_.status = true;
occbin_.info= {options_, oo_, M_, occbin_options, TTx, RRx, CCx,T0,R0};
occbin_.info= {options_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, M_, occbin_options, TTx, RRx, CCx,T0,R0};
else
% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,oo_.dr, M_.params] = dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state,'restrict');
[T,R,SteadyState,info,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,'restrict');
occbin_.status = false;
end
@ -249,7 +249,7 @@ if info(1)
end
% check endogenous prior restrictions
info=endogenous_prior_restrictions(T,R,M_,options_,oo_);
info=endogenous_prior_restrictions(T,R,M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if info(1)
fval = Inf;
info(4)=info(2);
@ -324,7 +324,7 @@ switch options_.lik_init
Pstar=lyapunov_solver(T,R,Q,options_);
Pinf = [];
a = zeros(mm,1);
a=set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a=set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1=T*a; %set state prediction for first Kalman step;
if options_.occbin.likelihood.status
@ -344,7 +344,7 @@ switch options_.lik_init
Pstar = options_.Harvey_scale_factor*eye(mm);
Pinf = [];
a = zeros(mm,1);
a = set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a; %set state prediction for first Kalman step;
if options_.occbin.likelihood.status
Z =zeros(length(bayestopt_.mf),size(T,1));
@ -378,7 +378,7 @@ switch options_.lik_init
if (kalman_algo==3)
% Multivariate Diffuse Kalman Filter
a = zeros(mm,1);
a = set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a; %set state prediction for first Kalman step;
Pstar0 = Pstar; % store Pstar
if no_missing_data_flag
@ -442,7 +442,7 @@ switch options_.lik_init
end
a = zeros(mmm,1);
a = set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a;
[dLIK,dlik,a_0_given_tm1,Pstar] = univariate_kalman_filter_d(dataset_info.missing.aindex,...
dataset_info.missing.number_of_observations,...
@ -479,7 +479,7 @@ switch options_.lik_init
end
Pinf = [];
a = zeros(mm,1);
a = set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a;
if options_.occbin.likelihood.status
Z =zeros(length(bayestopt_.mf),size(T,1));
@ -509,7 +509,7 @@ switch options_.lik_init
Pstar(stable, stable) = Pstar_tmp;
Pinf = [];
a = zeros(mm,1);
a = set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a;
if options_.occbin.likelihood.status
Z =zeros(length(bayestopt_.mf),size(T,1));
@ -541,9 +541,9 @@ if analytic_derivation
end
DLIK = [];
AHess = [];
iv = oo_.dr.restrict_var_list;
if nargin<10 || isempty(derivatives_info)
[A,B,nou,nou,oo_.dr, M_.params] = dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
iv = dr.restrict_var_list;
if nargin<13 || isempty(derivatives_info)
[A,B,nou,nou,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if ~isempty(estim_params_.var_exo)
indexo=estim_params_.var_exo(:,1);
else
@ -561,14 +561,14 @@ if analytic_derivation
old_analytic_derivation_mode = options_.analytic_derivation_mode;
options_.analytic_derivation_mode = kron_flag;
if full_Hess
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indparam, indexo, [], true);
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], true);
indD2T = reshape(1:M_.endo_nbr^2, M_.endo_nbr, M_.endo_nbr);
indD2Om = dyn_unvech(1:M_.endo_nbr*(M_.endo_nbr+1)/2);
D2T = DERIVS.d2KalmanA(indD2T(iv,iv),:);
D2Om = DERIVS.d2Om(dyn_vech(indD2Om(iv,iv)),:);
D2Yss = DERIVS.d2Yss(iv,:,:);
else
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indparam, indexo, [], false);
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], false);
end
DT = zeros(M_.endo_nbr, M_.endo_nbr, size(DERIVS.dghx,3));
DT(:,M_.nstatic+(1:M_.nspred),:) = DERIVS.dghx;
@ -912,7 +912,7 @@ else
end
if options_.prior_restrictions.status
tmp = feval(options_.prior_restrictions.routine, M_, oo_, options_, dataset_, dataset_info);
tmp = feval(options_.prior_restrictions.routine, M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
fval = fval - tmp;
end
@ -942,14 +942,14 @@ if analytic_derivation==0 && nargout>3
DLIK=[-lnprior; lik(:)];
end
function a=set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_)
% function a=set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_)
function a=set_Kalman_starting_values(a,M_,dr,options_,bayestopt_)
% function a=set_Kalman_starting_values(a,M_,dr,options_,bayestopt_)
% Sets initial states guess for Kalman filter/smoother based on M_.filter_initial_state
%
% INPUTS
% o a [double] (p*1) vector of states
% o M_ [structure] decribing the model
% o oo_ [structure] storing the results
% o dr [structure] storing the decision rules
% o options_ [structure] describing the options
% o bayestopt_ [structure] describing the priors
%
@ -957,13 +957,13 @@ function a=set_Kalman_starting_values(a,M_,oo_,options_,bayestopt_)
% o a [double] (p*1) vector of set initial states
if isfield(M_,'filter_initial_state') && ~isempty(M_.filter_initial_state)
state_indices=oo_.dr.order_var(oo_.dr.restrict_var_list(bayestopt_.mf0));
state_indices=dr.order_var(dr.restrict_var_list(bayestopt_.mf0));
for ii=1:size(state_indices,1)
if ~isempty(M_.filter_initial_state{state_indices(ii),1})
if options_.loglinear && ~options_.logged_steady_state
a(bayestopt_.mf0(ii)) = log(eval(M_.filter_initial_state{state_indices(ii),2})) - log(oo_.dr.ys(state_indices(ii)));
a(bayestopt_.mf0(ii)) = log(eval(M_.filter_initial_state{state_indices(ii),2})) - log(dr.ys(state_indices(ii)));
elseif ~options_.loglinear && ~options_.logged_steady_state
a(bayestopt_.mf0(ii)) = eval(M_.filter_initial_state{state_indices(ii),2}) - oo_.dr.ys(state_indices(ii));
a(bayestopt_.mf0(ii)) = eval(M_.filter_initial_state{state_indices(ii),2}) - dr.ys(state_indices(ii));
else
error(['The steady state is logged. This should not happen. Please contact the developers'])
end
@ -971,7 +971,7 @@ if isfield(M_,'filter_initial_state') && ~isempty(M_.filter_initial_state)
end
end
function occbin_options = set_occbin_options(options_, M_)
function occbin_options = set_occbin_options(options_)
% this builds the opts_simul options field needed by occbin.solver
occbin_options.opts_simul = options_.occbin.simul;

12
matlab/dsge_var_likelihood.m
View File

@ -1,4 +1,5 @@
function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_u_tilde,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DataSetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_u_tilde,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DataSetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_u_tilde,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DataSetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% Evaluates the posterior kernel of the BVAR-DSGE model.
%
% INPUTS
@ -11,7 +12,10 @@ function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_
% o EstimatedParameters [structure] characterizing parameters to be estimated
% o BayesInfo [structure] describing the priors
% o BoundsInfo [structure] containing prior bounds
% o DynareResults [structure] storing the results
% o dr [structure] Reduced form model.
% o endo_steady_state [vector] steady state value for endogenous variables
% o exo_steady_state [vector] steady state value for exogenous variables
% o exo_det_steady_state [vector] steady state value for exogenous deterministic variables
%
% OUTPUTS
% o fval [double] Value of the posterior kernel at xparam1.
@ -45,7 +49,7 @@ function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_
% SPECIAL REQUIREMENTS
% None.
% Copyright © 2006-2021 Dynare Team
% Copyright © 2006-2023Dynare Team
%
% This file is part of Dynare.
%
@ -136,7 +140,7 @@ end
% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
% state equation
[T,R,SteadyState,info] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state,'restrict');
[T,R,SteadyState,info] = dynare_resolve(Model,DynareOptions, dr, endo_steady_state, exo_steady_state, exo_det_steady_state,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)

180
matlab/dynare_estimation_1.m
View File

@ -93,7 +93,7 @@ if options_.order > 1
end
end
%% set objective function
%% set objective function
if ~options_.dsge_var
if options_.particle.status
objective_function = str2func('non_linear_dsge_likelihood');
@ -169,12 +169,12 @@ catch % if check fails, provide info on using calibration if present
rethrow(e);
end
%% Run smoother if no estimation or mode-finding are requested
%% Run smoother if no estimation or mode-finding are requested
if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
if options_.order==1 && ~options_.particle.status
if options_.smoother
if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_, atT, innov] = occbin.IVF_posterior(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_.dr, atT, innov] = occbin.IVF_posterior(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if ismember(info(1),[303,304,306])
fprintf('\nIVF: smoother did not succeed. No results will be written to oo_.\n')
else
@ -224,71 +224,71 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
for optim_iter = 1:length(optimizer_vec)
current_optimizer = optimizer_vec{optim_iter};
[xparam1, fval, ~, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,current_optimizer,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
[xparam1, fval, ~, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,current_optimizer,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
if isnumeric(current_optimizer)
if current_optimizer==5
newratflag = new_rat_hess_info.newratflag;
new_rat_hess_info = new_rat_hess_info.new_rat_hess_info;
newratflag = new_rat_hess_info.newratflag;
new_rat_hess_info = new_rat_hess_info.new_rat_hess_info;
elseif current_optimizer==6 %save scaling factor
save([M_.dname filesep 'Output' filesep M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
options_.mh_jscale = Scale;
bayestopt_.jscale(:) = options_.mh_jscale;
end
end
if ~isnumeric(current_optimizer) || ~isequal(current_optimizer,6) %always already computes covariance matrix
if options_.cova_compute == 1 %user did not request covariance not to be computed
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
[~,~,~,~,hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(current_optimizer) || ~(isequal(current_optimizer,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
% enter here if i) not mode_compute_5, ii) if mode_compute_5 and newratflag==1;
% with flag==0 or 2 and dsge_likelihood, we force to use
% the hessian from outer product gradient of optimizer 5 below
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(current_optimizer) && isequal(current_optimizer,5)
% other numerical hessian options available with optimizer
% 5 and dsge_likelihood
%
% if newratflag == 0
% compute outer product gradient of optimizer 5
%
% if newratflag == 2
% compute 'mixed' outer product gradient of optimizer 5
% with diagonal elements computed with numerical second order derivatives
%
% uses univariate filters, so to get max # of available
% densities for outer product gradient
kalman_algo0 = options_.kalman_algo;
compute_hessian = 1;
if ~((options_.kalman_algo == 2) || (options_.kalman_algo == 4))
options_.kalman_algo=2;
if options_.lik_init == 3
options_.kalman_algo=4;
end
elseif newratflag==0 % hh already contains outer product gradient with univariate filter
compute_hessian = 0;
end
if compute_hessian
crit = options_.newrat.tolerance.f;
newratflag = newratflag>0;
hh = reshape(mr_hessian(xparam1,objective_function,fval,newratflag,crit,new_rat_hess_info,[bounds.lb bounds.ub],bayestopt_.p2,0,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
end
options_.kalman_algo = kalman_algo0;
save([M_.dname filesep 'Output' filesep M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
options_.mh_jscale = Scale;
bayestopt_.jscale(:) = options_.mh_jscale;
end
end
end
parameter_names = bayestopt_.name;
if ~isnumeric(current_optimizer) || ~isequal(current_optimizer,6) %always already computes covariance matrix
if options_.cova_compute == 1 %user did not request covariance not to be computed
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
[~,~,~,~,hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(current_optimizer) || ~(isequal(current_optimizer,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
% enter here if i) not mode_compute_5, ii) if mode_compute_5 and newratflag==1;
% with flag==0 or 2 and dsge_likelihood, we force to use
% the hessian from outer product gradient of optimizer 5 below
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_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
else
hh = hessian(objective_function, xparam1, options_.gstep, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
hh = reshape(hh, nx, nx);
elseif isnumeric(current_optimizer) && isequal(current_optimizer,5)
% other numerical hessian options available with optimizer
% 5 and dsge_likelihood
%
% if newratflag == 0
% compute outer product gradient of optimizer 5
%
% if newratflag == 2
% compute 'mixed' outer product gradient of optimizer 5
% with diagonal elements computed with numerical second order derivatives
%
% uses univariate filters, so to get max # of available
% densities for outer product gradient
kalman_algo0 = options_.kalman_algo;
compute_hessian = 1;
if ~((options_.kalman_algo == 2) || (options_.kalman_algo == 4))
options_.kalman_algo=2;
if options_.lik_init == 3
options_.kalman_algo=4;
end
elseif newratflag==0 % hh already contains outer product gradient with univariate filter
compute_hessian = 0;
end
if compute_hessian
crit = options_.newrat.tolerance.f;
newratflag = newratflag>0;
hh = reshape(mr_hessian(xparam1,objective_function,fval,newratflag,crit,new_rat_hess_info,[bounds.lb bounds.ub],bayestopt_.p2,0,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state), nx, nx);
end
options_.kalman_algo = kalman_algo0;
end
end
end
parameter_names = bayestopt_.name;
end
if options_.cova_compute || current_optimizer==5 || current_optimizer==6
save([M_.dname filesep 'Output' filesep M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
@ -306,7 +306,7 @@ if options_.mode_check.status && ~options_.mh_posterior_mode_estimation
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 0;
mode_check(objective_function,xparam1,hh,options_,M_,estim_params_,bayestopt_,bounds,false,...
dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_);
dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
options_.analytic_derivation = ana_deriv_old;
end
@ -344,12 +344,12 @@ end
if options_.particle.status && isfield(options_.particle,'posterior_sampler')
if strcmpi(options_.particle.posterior_sampler,'Herbst_Schorfheide')
Herbst_Schorfheide_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
Herbst_Schorfheide_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
elseif strcmpi(options_.particle.posterior_sampler,'DSMH')
DSMH_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
DSMH_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
end
end
if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
% display results table and store parameter estimates and standard errors in results
oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Posterior', 'posterior');
@ -358,7 +358,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
estim_params_nbr = size(xparam1,1);
if ispd(invhess)
log_det_invhess = log(det(invhess./(stdh*stdh')))+2*sum(log(stdh));
likelihood = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
likelihood = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
else
oo_.MarginalDensity.LaplaceApproximation = NaN;
@ -369,7 +369,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
end
if options_.dsge_var
[~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
@ -391,7 +391,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
if options_.mh_tune_jscale.status
if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_);
dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
bayestopt_.jscale(:) = options_.mh_jscale;
fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
else
@ -449,9 +449,9 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
elseif options_.load_mh_file && options_.load_results_after_load_mh
%% load fields from previous MCMC run stored in results-file
field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
'prior_density',...%fields set by PlotPosteriorDistributions
};
'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
'prior_density',...%fields set by PlotPosteriorDistributions
};
for field_iter=1:size(field_names,2)
if isfield(oo_load_mh.oo_,field_names{1,field_iter})
oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
@ -479,23 +479,23 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
error('%s: I cannot compute the posterior moments for the endogenous variables!',dispString)
end
if options_.load_mh_file && options_.mh_replic==0 %user wants to recompute results
[MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
if ~info
generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
for ii=1:length(generic_post_data_file_name)
delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
end
% restore compatibility for loading pre-4.6.2 runs where estim_params_ was not saved; see 6e06acc7 and !1944
NumberOfDrawsFiles = length(dir([M_.dname '/metropolis/' M_.fname '_posterior_draws*' ]));
if NumberOfDrawsFiles>0
temp=load([M_.dname '/metropolis/' M_.fname '_posterior_draws1']);
if ~isfield(temp,'estim_params_')
for file_iter=1:NumberOfDrawsFiles
save([M_.dname '/metropolis/' M_.fname '_posterior_draws' num2str(file_iter)],'estim_params_','-append')
end
end
end
end
[MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
if ~info
generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
for ii=1:length(generic_post_data_file_name)
delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
end
% restore compatibility for loading pre-4.6.2 runs where estim_params_ was not saved; see 6e06acc7 and !1944
NumberOfDrawsFiles = length(dir([M_.dname '/metropolis/' M_.fname '_posterior_draws*' ]));
if NumberOfDrawsFiles>0
temp=load([M_.dname '/metropolis/' M_.fname '_posterior_draws1']);
if ~isfield(temp,'estim_params_')
for file_iter=1:NumberOfDrawsFiles
save([M_.dname '/metropolis/' M_.fname '_posterior_draws' num2str(file_iter)],'estim_params_','-append')
end
end
end
end
end
oo_ = compute_moments_varendo('posterior',options_,M_,oo_,estim_params_,var_list_);
end
@ -527,7 +527,7 @@ end
%Run and store classical smoother if needed
if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) ...
|| ~options_.smoother ) && ~options_.partial_information % to be fixed
|| ~options_.smoother ) && ~options_.partial_information % to be fixed
%% ML estimation, or posterior mode without Metropolis-Hastings or Metropolis without Bayesian smoothed variables
oo_=save_display_classical_smoother_results(xparam1,M_,oo_,options_,bayestopt_,dataset_,dataset_info,estim_params_);
end

35
matlab/endogenous_prior_restrictions.m
View File

@ -1,13 +1,16 @@
function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults)
function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
%[info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% Check for prior (sign) restrictions on irf's and theoretical moments
%
% INPUTS
% T [double] n*n state space matrix
% R [double] n*k matrix of shocks
% Model [structure]
% DynareOptions [structure]
% DynareResults [structure]
% T [double] n*n state space matrix
% R [double] n*k matrix of shocks
% Model [structure]
% DynareOptions [structure]
% dr [structure] Reduced form model.
% endo_steady_state [vector] steady state value for endogenous variables
% exo_steady_state [vector] steady state value for exogenous variables
% exo_det_steady_state [vector] steady state value for exogenous deterministic variables
%
% OUTPUTS
% info [double] check if prior restrictions are matched by the
% model and related info
@ -15,7 +18,7 @@ function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior
% info_moment [double] array of test checks for all individual moment restrictions
%
% Copyright © 2013-2018 Dynare Team
% Copyright © 2013-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -45,14 +48,13 @@ if ~isempty(endo_prior_restrictions.irf)
data_irf=cell(size(endo_prior_restrictions.irf,1),1);
if DynareOptions.order>1
error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
return
end
varlist = Model.endo_names(DynareResults.dr.order_var);
varlist = Model.endo_names(dr.order_var);
if isempty(T)
[T,R,SteadyState,infox,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
[T,R,SteadyState,infox,dr, Model.params] = dynare_resolve(Model,DynareOptions,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
else % check if T and R are given in the restricted form!!!
if size(T,1)<length(varlist)
varlist = varlist(DynareResults.dr.restrict_var_list);
varlist = varlist(dr.restrict_var_list);
end
% check if endo_prior_restrictions.irf{:,1} variables are in varlist
varlistok=1;
@ -62,8 +64,8 @@ if ~isempty(endo_prior_restrictions.irf)
end
end
if ~varlistok
varlist = Model.endo_names(DynareResults.dr.order_var);
[T,R,SteadyState,infox,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
varlist = Model.endo_names(dr.order_var);
[T,R,SteadyState,infox,dr, Model.params] = dynare_resolve(Model,DynareOptions,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
end
end
NT=1;
@ -105,7 +107,6 @@ end
if ~isempty(endo_prior_restrictions.moment)
if DynareOptions.order>1
error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
return
end
data_moment=cell(size(endo_prior_restrictions.moment,1),1);
var_list_ = endo_prior_restrictions.moment{1,1};
@ -137,7 +138,7 @@ if ~isempty(endo_prior_restrictions.moment)
end
end
DynareOptions.ar = max(abs(NTmin),NTmax);
[gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
[gamma_y,stationary_vars] = th_autocovariances(dr, ivar, Model, DynareOptions,1);
for t=NTmin:NTmax
RR = gamma_y{abs(t)+1};
if t==0

7
matlab/evaluate_likelihood.m
View File

@ -1,4 +1,5 @@
function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% Evaluate the logged likelihood at parameters.
%
% INPUTS
@ -22,7 +23,7 @@ function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_
% [2] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
% is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright © 2009-2017 Dynare Team
% Copyright © 2009-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -87,9 +88,9 @@ end
if options_.occbin.likelihood.status && options_.occbin.likelihood.inversion_filter
llik = -occbin.IVF_posterior(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
llik = -occbin.IVF_posterior(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
else
llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
ldens = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
llik = llik - ldens;

5
matlab/evaluate_smoother.m
View File

@ -1,4 +1,5 @@
function [oo_,M_,options_,bayestopt_,Smoothed_variables_declaration_order_deviation_form,initial_date]=evaluate_smoother(parameters,var_list,M_,oo_,options_,bayestopt_,estim_params_)
% [oo_,M_,options_,bayestopt_,Smoothed_variables_declaration_order_deviation_form,initial_date]=evaluate_smoother(parameters,var_list,M_,oo_,options_,bayestopt_,estim_params_)
% Evaluate the smoother at parameters.
%
% INPUTS
@ -38,7 +39,7 @@ function [oo_,M_,options_,bayestopt_,Smoothed_variables_declaration_order_deviat
% [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
% is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright © 2010-2020 Dynare Team
% Copyright © 2010-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -105,7 +106,7 @@ end
if options_.occbin.smoother.status
if options_.occbin.smoother.inversion_filter
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_, atT, innov] = occbin.IVF_posterior(parameters,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_.dr, atT, innov] = occbin.IVF_posterior(parameters,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if ismember(info(1),[303,304,306])
oo_.occbin.smoother.error_flag=1;
else

5
matlab/gsa/map_calibration.m
View File

@ -1,11 +1,12 @@
function map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResults, EstimatedParameters, BayesInfo)
% map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResults, EstimatedParameters, BayesInfo)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
% marco.ratto@ec.europa.eu
% Copyright © 2014-2016 European Commission
% Copyright © 2014-2018 Dynare Team
% Copyright © 2014-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -107,7 +108,7 @@ if init
[Tt,Rr,SteadyState,info,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state,'restrict');
end
if info(1)==0
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults);
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults.dr,DynareResults.steady_state,DynareResults.exo_steady_state,DynareResults.exo_det_steady_state);
if ~isempty(info_irf)
for ij=1:nbr_irf_restrictions
mat_irf{ij}(j,:)=data_irf{ij}(:,2)';

8
matlab/gsa/stab_map_.m
View File

@ -1,7 +1,5 @@
function x0 = stab_map_(OutputDirectoryName,opt_gsa)
%
% function x0 = stab_map_(OutputDirectoryName)
%
% x0 = stab_map_(OutputDirectoryName,opt_gsa)
% Mapping of stability regions in the prior ranges applying
% Monte Carlo filtering techniques.
%
@ -35,7 +33,7 @@ function x0 = stab_map_(OutputDirectoryName,opt_gsa)
% marco.ratto@ec.europa.eu
% Copyright © 2012-2016 European Commission
% Copyright © 2012-2018 Dynare Team
% Copyright © 2012-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -296,7 +294,7 @@ if fload==0
nboth = dr_.nboth;
nfwrd = dr_.nfwrd;
end
info=endogenous_prior_restrictions(Tt,Rr,M_,options_,oo_);
info=endogenous_prior_restrictions(Tt,Rr,M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
infox(j,1)=info(1);
if info(1)
inorestriction(j)=j;

4
matlab/identification_analysis.m
View File

@ -73,7 +73,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% * stoch_simul
% * vec
% =========================================================================
% Copyright © 2008-2021 Dynare Team
% Copyright © 2008-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -296,7 +296,7 @@ if info(1) == 0 %no errors in solution
derivatives_info.no_DLIK = 1;
bounds = prior_bounds(bayestopt_, options_.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
%note that for order>1 we do not provide any information on DT,DYss,DOM in derivatives_info, such that dsge_likelihood creates an error. Therefore the computation will be based on simulated_moment_uncertainty for order>1.
[fval, info, cost_flag, DLIK, AHess, ys, trend_coeff, M_, options_, bayestopt_, oo_] = dsge_likelihood(params', dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_, derivatives_info); %non-used output variables need to be set for octave for some reason
[fval, info, cost_flag, DLIK, AHess, ys, trend_coeff, M_, options_, bayestopt_, oo_.dr] = dsge_likelihood(params', dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr, oo_.steady_state,oo_.exo_steady_state, oo_.exo_det_steady_state. derivatives_info); %non-used output variables need to be set for octave for some reason
%note that for the order of parameters in AHess we have: stderr parameters come first, corr parameters second, model parameters third. the order within these blocks corresponds to the order specified in the estimated_params block
options_.analytic_derivation = analytic_derivation; %reset option
AHess = -AHess; %take negative of hessian

2
matlab/initial_estimation_checks.m
View File

@ -204,7 +204,7 @@ if ~isequal(DynareOptions.mode_compute,11) || ...
%purpose of checking stochastic singularity
use_univariate_filters_if_singularity_is_detected_old=DynareOptions.use_univariate_filters_if_singularity_is_detected;
DynareOptions.use_univariate_filters_if_singularity_is_detected=0;
[fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
[fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults.dr,DynareResults.steady_state,DynareResults.exo_steady_state,DynareResults.exo_det_steady_state);
if info(1)==50
fprintf('\ninitial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.\n')
fprintf('initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance\n')

30
matlab/kalman/likelihood/missing_observations_kalman_filter.m
View File

@ -1,4 +1,5 @@
function [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,number_of_observations,no_more_missing_observations,Y,start,last,a,P,kalman_tol,riccati_tol,rescale_prediction_error_covariance,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,occbin_)
% [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,number_of_observations,no_more_missing_observations,Y,start,last,a,P,kalman_tol,riccati_tol,rescale_prediction_error_covariance,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,occbin_)
% Computes the likelihood of a state space model in the case with missing observations.
%
% INPUTS
@ -32,7 +33,7 @@ function [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,numbe
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright © 2004-2021 Dynare Team
% Copyright © 2004-2023 Dynare Team
%
% This file is part of Dynare.
%
@ -100,9 +101,12 @@ if occbin_.status
vv = zeros(pp,last);
options_=occbin_.info{1};
oo_=occbin_.info{2};
M_=occbin_.info{3};
occbin_options=occbin_.info{4};
dr=occbin_.info{2};
endo_steady_state=occbin_.info{3};
exo_steady_state=occbin_.info{4};
exo_det_steady_state=occbin_.info{5};
M_=occbin_.info{6};
occbin_options=occbin_.info{7};
opts_regime.regime_history = occbin_options.opts_simul.init_regime;
opts_regime.binding_indicator = occbin_options.opts_simul.init_binding_indicator;
if t>1
@ -113,13 +117,13 @@ if occbin_.status
if isempty(opts_regime.binding_indicator) && isempty(opts_regime.regime_history)
opts_regime.binding_indicator=zeros(last+2,M_.occbin.constraint_nbr);
end
[~, ~, ~, regimes_] = occbin.check_regimes([], [], [], opts_regime, M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if length(occbin_.info)>4
TT=occbin_.info{5};
RR=occbin_.info{6};
CC=occbin_.info{7};
T0=occbin_.info{8};
R0=occbin_.info{9};
[~, ~, ~, regimes_] = occbin.check_regimes([], [], [], opts_regime, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if length(occbin_.info)>7
TT=occbin_.info{8};
RR=occbin_.info{9};
CC=occbin_.info{10};
T0=occbin_.info{11};
R0=occbin_.info{12};
TT = cat(3,TT,T);
RR = cat(3,RR,R);
CC = cat(2,CC,zeros(mm,1));
@ -254,12 +258,12 @@ while notsteady && t<=last
RR01 = cat(3,R,RR(:,:,1));
CC01 = zeros(size(CC,1),2);
CC01(:,2) = CC(:,1);
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a00, a10, P00, P10, data_index0, Z, v0, Y0, H, Qt, T0, R0, TT01, RR01, CC01, regimes_(t:t+1), M_, oo_, options_, occbin_options);
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a00, a10, P00, P10, data_index0, Z, v0, Y0, H, Qt, T0, R0, TT01, RR01, CC01, regimes_(t:t+1), M_, dr, endo_steady_state,exo_steady_state,exo_det_steady_state, options_, occbin_options);
else
if isqvec
Qt = Qvec(:,:,t-1:t+1);
end
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a0(:,t-1),a1(:,t-1:t),P0(:,:,t-1),P1(:,:,t-1:t),data_index(t-1:t),Z,vv(:,t-1:t),Y(:,t-1:t),H,Qt,T0,R0,TT(:,:,t-1:t),RR(:,:,t-1:t),CC(:,t-1:t),regimes_(t:t+1),M_,oo_,options_,occbin_options);
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a0(:,t-1),a1(:,t-1:t),P0(:,:,t-1),P1(:,:,t-1:t),data_index(t-1:t),Z,vv(:,t-1:t),Y(:,t-1:t),H,Qt,T0,R0,TT(:,:,t-1:t),RR(:,:,t-1:t),CC(:,t-1:t),regimes_(t:t+1),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
end
if info
if options_.debug

47
matlab/missing_DiffuseKalmanSmootherH3_Z.m
View File

@ -163,9 +163,12 @@ else
isoccbin = 1;
Qt = repmat(Q,[1 1 3]);
options_=occbin_.info{1};
oo_=occbin_.info{2};
M_=occbin_.info{3};
occbin_options=occbin_.info{4};
dr=occbin_.info{2};
endo_steady_state=occbin_.info{3};
exo_steady_state=occbin_.info{4};
exo_det_steady_state=occbin_.info{5};
M_=occbin_.info{6};
occbin_options=occbin_.info{7};
opts_regime = occbin_options.opts_regime;
% first_period_occbin_update = inf;
if isfield(opts_regime,'regime_history') && ~isempty(opts_regime.regime_history)
@ -174,29 +177,29 @@ else
opts_regime.binding_indicator=zeros(smpl+2,M_.occbin.constraint_nbr);
end
occbin_options.opts_regime = opts_regime;
[~, ~, ~, regimes_] = occbin.check_regimes([], [], [], opts_regime, M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if length(occbin_.info)>4
if length(occbin_.info)==6 && options_.smoother_redux
[~, ~, ~, regimes_] = occbin.check_regimes([], [], [], opts_regime, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if length(occbin_.info)>7
if length(occbin_.info)==9 && options_.smoother_redux
TT=repmat(T,1,1,smpl+1);
RR=repmat(R,1,1,smpl+1);
CC=repmat(zeros(mm,1),1,smpl+1);
T0=occbin_.info{5};
R0=occbin_.info{6};
T0=occbin_.info{8};
R0=occbin_.info{9};
else
TT=occbin_.info{5};
RR=occbin_.info{6};
CC=occbin_.info{7};
TT=occbin_.info{8};
RR=occbin_.info{9};
CC=occbin_.info{10};
% TT = cat(3,TT,T);
% RR = cat(3,RR,R);
% CC = cat(2,CC,zeros(mm,1));
if options_.smoother_redux
my_order_var = oo_.dr.restrict_var_list;
my_order_var = dr.restrict_var_list;
CC = CC(my_order_var,:);
RR = RR(my_order_var,:,:);
TT = TT(my_order_var,my_order_var,:);
T0=occbin_.info{8};
R0=occbin_.info{9};
T0=occbin_.info{11};
R0=occbin_.info{12};
end
if size(TT,3)<(smpl+1)
TT=repmat(T,1,1,smpl+1);
@ -368,12 +371,12 @@ while notsteady && t<smpl
RR01 = cat(3,R,RR(:,:,1));
CC01 = zeros(size(CC,1),2);
CC01(:,2) = CC(:,1);
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, tmp, error_flag, M_, aha, etaha,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a10,P0,P10,data_index0,Z,v0,Fi0,Ki0,Y0,H,Qt,T0,R0,TT01,RR01,CC01,regimes_(t:t+1),M_,oo_,options_,occbin_options,kalman_tol,nk);
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, tmp, error_flag, M_, aha, etaha,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a10,P0,P10,data_index0,Z,v0,Fi0,Ki0,Y0,H,Qt,T0,R0,TT01,RR01,CC01,regimes_(t:t+1),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
else
if isqvec
Qt = Qvec(:,:,t-1:t+1);
end
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, tmp, error_flag, M_, aha, etaha,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a(:,t-1),a1(:,t-1:t),P(:,:,t-1),P1(:,:,t-1:t),data_index(t-1:t),Z,v(:,t-1:t),Fi(:,t-1),Ki(:,:,t-1),Y(:,t-1:t),H,Qt,T0,R0,TT(:,:,t-1:t),RR(:,:,t-1:t),CC(:,t-1:t),regimes_(t:t+1),M_,oo_,options_,occbin_options,kalman_tol,nk);
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, tmp, error_flag, M_, aha, etaha,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a(:,t-1),a1(:,t-1:t),P(:,:,t-1),P1(:,:,t-1:t),data_index(t-1:t),Z,v(:,t-1:t),Fi(:,t-1),Ki(:,:,t-1),Y(:,t-1:t),H,Qt,T0,R0,TT(:,:,t-1:t),RR(:,:,t-1:t),CC(:,t-1:t),regimes_(t:t+1),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if ~error_flag
regimes_(t:t+2)=tmp;
@ -498,15 +501,15 @@ while notsteady && t<smpl
opts_simul.SHOCKS = zeros(nk,M_.exo_nbr);
if smoother_redux
tmp=zeros(M_.endo_nbr,1);
tmp(oo_.dr.restrict_var_list)=a(:,t);
opts_simul.endo_init = tmp(oo_.dr.inv_order_var);
tmp(dr.restrict_var_list)=a(:,t);
opts_simul.endo_init = tmp(dr.inv_order_var);
else
opts_simul.endo_init = a(oo_.dr.inv_order_var,t);
opts_simul.endo_init = a(dr.inv_order_var,t);
end
opts_simul.init_regime = []; %regimes_(t);
opts_simul.waitbar=0;
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
[~, out, ss] = occbin.solver(M_,options_,dr,steady_state,exo_steady_state,exo_det_steady_state);
end
for jnk=1:nk
if filter_covariance_flag
@ -518,9 +521,9 @@ while notsteady && t<smpl
if jnk>1
if isoccbin && (t>=first_period_occbin_update || isinf(first_period_occbin_update))
if smoother_redux
aK(jnk,:,t+jnk) = out.piecewise(jnk,oo_.dr.order_var(oo_.dr.restrict_var_list)) - out.ys(oo_.dr.order_var(oo_.dr.restrict_var_list))';
aK(jnk,:,t+jnk) = out.piecewise(jnk,dr.order_var(dr.restrict_var_list)) - out.ys(dr.order_var(dr.restrict_var_list))';
else
aK(jnk,oo_.dr.inv_order_var,t+jnk) = out.piecewise(jnk,:) - out.ys';
aK(jnk,dr.inv_order_var,t+jnk) = out.piecewise(jnk,:) - out.ys';
end
else
aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));

13
matlab/non_linear_dsge_likelihood.m
View File

@ -1,4 +1,4 @@
function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,options_,M_,EstimatedParameters,bayestopt_,BoundsInfo,oo_)
function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,dr] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,options_,M_,EstimatedParameters,bayestopt_,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% Evaluates the posterior kernel of a dsge model using a non linear filter.
%
@ -11,7 +11,10 @@ function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,oo
% - EstimatedParameters [struct] Matlab's structure describing the estimated_parameters
% - bayestopt_ [struct] Matlab's structure describing the priors
% - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values
% - oo_ [struct] Matlab's structure gathering the results
% - dr [structure] Reduced form model.
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
%
% OUTPUTS
% - fval [double] scalar, value of the likelihood or posterior kernel.
@ -24,7 +27,7 @@ function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,oo
% - M_ [struct] Updated M_ structure described in INPUTS section.
% - options_ [struct] Updated options_ structure described in INPUTS section.
% - bayestopt_ [struct] See INPUTS section.
% - oo_ [struct] Updated oo_ structure described in INPUTS section.
% - dr [struct] decision rule structure described in INPUTS section.
% Copyright © 2010-2023 Dynare Team
%
@ -80,7 +83,7 @@ end
%------------------------------------------------------------------------------
% Linearize the model around the deterministic steadystate and extract the matrices of the state equation (T and R).
[dr, info, M_.params] = resol(0, M_, options_, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[dr, info, M_.params] = resol(0, M_, options_, dr , endo_steady_state, exo_steady_state, exo_det_steady_state);
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
@ -163,7 +166,7 @@ switch options_.particle.initialization
options_.periods = 5000;
old_DynareOptionspruning = options_.pruning;
options_.pruning = options_.particle.pruning;
y_ = simult(oo_.steady_state, dr,M_,options_);
y_ = simult(endo_steady_state, dr,M_,options_);
y_ = y_(dr.order_var(state_variables_idx),2001:5000); %state_variables_idx is in dr-order while simult_ is in declaration order
if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ options_.pruning
fval = Inf;

5
matlab/posterior_sampler.m
View File

@ -104,7 +104,10 @@ localVars = struct('TargetFun', TargetFun, ...
'M_',M_, ...
'bayestopt_', bayestopt_, ...
'estim_params_', estim_params_, ...
'oo_', oo_,...
'dr', oo_.dr,...
'endo_steady_state', oo_.steady_state,...
'exo_steady_state', oo_.exo_steady_state,...
'exo_det_steady_state', oo_.exo_det_steady_state,...
'varargin',[]);
if strcmp(sampler_options.posterior_sampling_method,'tailored_random_block_metropolis_hastings')

8
matlab/posterior_sampler_core.m
View File

@ -81,7 +81,11 @@ bayestopt_ = myinputs.bayestopt_;
estim_params_ = myinputs.estim_params_;
options_ = myinputs.options_;
M_ = myinputs.M_;
oo_ = myinputs.oo_;
dr = myinputs.dr;
endo_steady_state = myinputs.endo_steady_state;
exo_steady_state=myinputs.exo_steady_state;
exo_det_steady_state=myinputs.exo_det_steady_state;
% Necessary only for remote computing!
if whoiam
% initialize persistent variables in priordens()
@ -191,7 +195,7 @@ for curr_block = fblck:nblck
sampler_options.curr_block = curr_block;
while draw_iter <= nruns(curr_block)
[par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun, last_draw(curr_block,:), last_posterior(curr_block), sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
[par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun, last_draw(curr_block,:), last_posterior(curr_block), sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
x2(draw_index_current_file,:) = par;
last_draw(curr_block,:) = par;

4
matlab/posterior_sampler_initialization.m
View File

@ -202,7 +202,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
end
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2(j,new_estimated_parameters) = candidate(new_estimated_parameters);
ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if ~isfinite(ilogpo2(j)) % if returned log-density is
% Inf or Nan (penalized value)
validate = 0;
@ -256,7 +256,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
candidate = transpose(xparam1(:));%
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2 = candidate;
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
fprintf('%s: Initialization at the posterior mode.\n\n',dispString);
fprintf(fidlog,[' Blck ' int2str(1) 'params:\n']);
for i=1:length(ix2(1,:))

2
matlab/prior_sampler.m
View File

@ -98,7 +98,7 @@ while iteration < NumberOfSimulations
M_ = set_all_parameters(params, estim_params_, M_);
[T, R, ~, INFO, oo_.dr,M_.params] = dynare_resolve(M_, options_, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state, 'restrict');
if ~INFO(1)
INFO=endogenous_prior_restrictions(T,R,M_,options_,oo_);
INFO=endogenous_prior_restrictions(T,R,M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
file_line_number = file_line_number + 1;
iteration = iteration + 1;

2
matlab/save_display_classical_smoother_results.m
View File

@ -36,7 +36,7 @@ gend= dataset_.nobs;
n_varobs = length(options_.varobs);
if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_, atT, innov] = occbin.IVF_posterior(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
[~, info, ~, ~, ~, ~, ~, ~, ~, ~, oo_.dr, atT, innov] = occbin.IVF_posterior(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if ismember(info(1),[303,304,306])
fprintf('\nIVF: smoother did not succeed. No results will be written to oo_.\n')
else

6
tests/estimation/system_prior_restriction/Gali_2015_prior_restrictions.m
View File

@ -1,12 +1,12 @@
function log_prior_val=Gali_2015_prior_restrictions(M_, oo_, options_, dataset_, dataset_info);
% function prior_val=Gali_2015_prior_restrictions(M_, oo_, options_, dataset_, dataset_info);
function log_prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
% function prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
% Example of a _prior_restrictions-file automatically called during
% estimation
% It imposes a prior of the slope of the New Keynesian Phillips Curve of
% 0.03. As the slope is a composite of other parameters with independent
% priors, a separate function is required to do this.
% Copyright © 2021 Dynare Team
% Copyright © 2021-2023 Dynare Team
%
% This file is part of Dynare.
%