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time-shift
Willi Mutschler 2020-01-16 14:43:39 +01:00
parent a62e69cf39
commit 46c4dea559
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matlab/dynare_identification.m
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@ -1,17 +1,14 @@
function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0) function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
%function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0) %function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function is called, when the user specifies identification(...); in % This function is called, when the user specifies identification(...); in the mod file. It prepares all identification analysis:
% the mod file. It prepares all identification analysis, i.e. % (1) set options, local and persistent variables for a new identification
% (1) sets options, local and persistent variables for a new identification % analysis either for a single point or a MC Sample. It also displays and plots the results
% analysis either for a single point or a MC Sample. It also displays
% and plots the results.
% or this function can be used to
% (2) load, display and plot a previously saved identification analysis % (2) load, display and plot a previously saved identification analysis
% Note: This function does not output the arguments to the workspace if only called by %
% "identification" in the mod file, but saves results to the folder identification. % Note 1: This function does not output the arguments to the workspace, but saves results to the folder identification
% If you want to use this function directly in the mod file and workspace, you still have % Note 2: If you want to use this function directly in the mod file and workspace, you still have
% to put identification once in the mod file, otherwise the preprocessor won't compute all necessary objects % to put identification in your mod file, otherwise the preprocessor won't provide all necessary objects
% ========================================================================= % =========================================================================
% INPUTS % INPUTS
% * options_ident [structure] identification options % * options_ident [structure] identification options
@ -49,7 +46,7 @@ function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, ST
% * skipline % * skipline
% * vnorm % * vnorm
% ========================================================================= % =========================================================================
% Copyright (C) 2010-2019 Dynare Team % Copyright (C) 2010-2020 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
@ -92,11 +89,11 @@ else
warning('off','MATLAB:log:logOfZero'); warning('off','MATLAB:log:logOfZero');
end end
%% Set all options and create objects %% Set all options in options_ident and create objects
options_ident = set_default_option(options_ident,'gsa_sample_file',0); options_ident = set_default_option(options_ident,'gsa_sample_file',0);
% 0: do not use sample file. % 0: do not use sample file
% 1: triggers gsa prior sample. % 1: triggers gsa prior sample
% 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in dynare_sensitivity options). % 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in dynare_sensitivity options)
% FILENAME: use sample file in provided path % FILENAME: use sample file in provided path
options_ident = set_default_option(options_ident,'parameter_set','prior_mean'); options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
% 'calibration': use values in M_.params and M_.Sigma_e to update estimated stderr, corr and model parameters (get_all_parameters) % 'calibration': use values in M_.params and M_.Sigma_e to update estimated stderr, corr and model parameters (get_all_parameters)
@ -108,8 +105,8 @@ options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
options_ident = set_default_option(options_ident,'load_ident_files',0); options_ident = set_default_option(options_ident,'load_ident_files',0);
% 1: load previously computed analysis from identification/fname_identif.mat % 1: load previously computed analysis from identification/fname_identif.mat
options_ident = set_default_option(options_ident,'useautocorr',0); options_ident = set_default_option(options_ident,'useautocorr',0);
% 1: use autocorrelations in Iskrev (2010)'s criteria % 1: use autocorrelations in Iskrev (2010)'s theoretical second moments criteria
% 0: use autocovariances in Iskrev (2010)'s criteria % 0: use autocovariances in Iskrev (2010)'s theoretical second moments criteria
options_ident = set_default_option(options_ident,'ar',1); options_ident = set_default_option(options_ident,'ar',1);
% number of lags to consider for autocovariances/autocorrelations in Iskrev (2010)'s criteria % number of lags to consider for autocovariances/autocorrelations in Iskrev (2010)'s criteria
options_ident = set_default_option(options_ident,'prior_mc',1); options_ident = set_default_option(options_ident,'prior_mc',1);
@ -118,9 +115,9 @@ options_ident = set_default_option(options_ident,'prior_range',0);
% 1: sample uniformly from prior ranges implied by the prior specifications (overwrites prior shape when prior_mc > 1) % 1: sample uniformly from prior ranges implied by the prior specifications (overwrites prior shape when prior_mc > 1)
% 0: sample from specified prior distributions (when prior_mc > 1) % 0: sample from specified prior distributions (when prior_mc > 1)
options_ident = set_default_option(options_ident,'periods',300); options_ident = set_default_option(options_ident,'periods',300);
% length of stochastic simulation to compute simulated moment uncertainty, when analytic Hessian is not available % length of stochastic simulation to compute simulated moment uncertainty (when analytic Hessian is not available)
options_ident = set_default_option(options_ident,'replic',100); options_ident = set_default_option(options_ident,'replic',100);
% number of replicas to compute simulated moment uncertainty, when analytic Hessian is not available % number of replicas to compute simulated moment uncertainty (when analytic Hessian is not available)
options_ident = set_default_option(options_ident,'advanced',0); options_ident = set_default_option(options_ident,'advanced',0);
% 1: show a more detailed analysis based on reduced-form model solution and dynamic model derivatives. Further, performs a brute force % 1: show a more detailed analysis based on reduced-form model solution and dynamic model derivatives. Further, performs a brute force
% search of the groups of parameters best reproducing the behavior of each single parameter. % search of the groups of parameters best reproducing the behavior of each single parameter.
@ -128,8 +125,7 @@ options_ident = set_default_option(options_ident,'normalize_jacobians',1);
% 1: normalize Jacobians by either rescaling each row by its largest element in absolute value or for Gram (or Hessian-type) matrices by transforming into correlation-type matrices % 1: normalize Jacobians by either rescaling each row by its largest element in absolute value or for Gram (or Hessian-type) matrices by transforming into correlation-type matrices
options_ident = set_default_option(options_ident,'grid_nbr',5000); options_ident = set_default_option(options_ident,'grid_nbr',5000);
% number of grid points in [-pi;pi] to approximate the integral to compute Qu and Tkachenko (2012)'s criteria % number of grid points in [-pi;pi] to approximate the integral to compute Qu and Tkachenko (2012)'s criteria
% note that grid_nbr needs to be even and actually we use (grid_nbr+1) points, as we add the 0 frequency and use symmetry, i.e. grid_nbr/2 % note that grid_nbr needs to be even and actually we use (grid_nbr+1) points, as we add the 0 frequency and use symmetry
% negative as well as grid_nbr/2 positive values to speed up the compuations
if mod(options_ident.grid_nbr,2) ~= 0 if mod(options_ident.grid_nbr,2) ~= 0
options_ident.grid_nbr = options_ident.grid_nbr+1; options_ident.grid_nbr = options_ident.grid_nbr+1;
fprintf('IDENTIFICATION: ''grid_nbr'' needs to be even, hence it is reset to %d\n',options_ident.grid_nbr) fprintf('IDENTIFICATION: ''grid_nbr'' needs to be even, hence it is reset to %d\n',options_ident.grid_nbr)
@ -143,7 +139,7 @@ options_ident = set_default_option(options_ident,'tol_deriv',1.e-8);
% tolerance level for selecting columns of non-zero derivatives % tolerance level for selecting columns of non-zero derivatives
options_ident = set_default_option(options_ident,'tol_sv',1.e-3); options_ident = set_default_option(options_ident,'tol_sv',1.e-3);
% tolerance level for selecting non-zero singular values in identification_checks.m % tolerance level for selecting non-zero singular values in identification_checks.m
%check whether to compute identification strength based on information matrix %check whether to compute identification strength based on information matrix
if ~isfield(options_ident,'no_identification_strength') if ~isfield(options_ident,'no_identification_strength')
options_ident.no_identification_strength = 0; options_ident.no_identification_strength = 0;
@ -175,20 +171,20 @@ else
options_ident.no_identification_spectrum = 1; options_ident.no_identification_spectrum = 1;
end end
%overwrite setting, as identification strength and advanced need criteria based on both reducedform and moments %overwrite setting, as identification strength and advanced need both reducedform and moments criteria
if (isfield(options_ident,'no_identification_strength') && options_ident.no_identification_strength == 0) || (options_ident.advanced == 1) if (isfield(options_ident,'no_identification_strength') && options_ident.no_identification_strength == 0) || (options_ident.advanced == 1)
options_ident.no_identification_reducedform = 0; options_ident.no_identification_reducedform = 0;
options_ident.no_identification_moments = 0; options_ident.no_identification_moments = 0;
end end
%overwrite setting, as dynare_sensitivity does not make use of spectrum and minimal system (yet) %overwrite setting, as dynare_sensitivity does not make use of spectrum and minimal system
if isfield(options_,'opt_gsa') && isfield(options_.opt_gsa,'identification') && options_.opt_gsa.identification == 1 if isfield(options_,'opt_gsa') && isfield(options_.opt_gsa,'identification') && options_.opt_gsa.identification == 1
options_ident.no_identification_minimal = 1; options_ident.no_identification_minimal = 1;
options_ident.no_identification_spectrum = 1; options_ident.no_identification_spectrum = 1;
end end
%Deal with non-stationary cases %Deal with non-stationary cases
if isfield(options_ident,'diffuse_filter') %set lik_init and options_ if isfield(options_ident,'diffuse_filter')
options_ident.lik_init=3; %overwrites any other lik_init set options_ident.lik_init=3; %overwrites any other lik_init set
options_.diffuse_filter=options_ident.diffuse_filter; %make options_ inherit diffuse filter; will overwrite any conflicting lik_init in dynare_estimation_init options_.diffuse_filter=options_ident.diffuse_filter; %make options_ inherit diffuse filter; will overwrite any conflicting lik_init in dynare_estimation_init
else else
@ -216,7 +212,7 @@ options_ident = set_default_option(options_ident,'analytic_derivation',1);
options_ident = set_default_option(options_ident,'order',1); options_ident = set_default_option(options_ident,'order',1);
% 1: first-order perturbation approximation, identification is based on linear state space system % 1: first-order perturbation approximation, identification is based on linear state space system
% 2: second-order perturbation approximation, identification is based on second-order pruned state space system % 2: second-order perturbation approximation, identification is based on second-order pruned state space system
% 3: third-order perturbation approximation, identification is based on third-order pruned state space system % 3: third-order perturbation approximation, identification is based on third-order pruned state space system
%overwrite values in options_, note that options_ is restored at the end of the function %overwrite values in options_, note that options_ is restored at the end of the function
if isfield(options_ident,'TeX') if isfield(options_ident,'TeX')
@ -302,14 +298,14 @@ options_.plot_priors = 0;
options_.smoother = 1; options_.smoother = 1;
options_.options_ident = []; options_.options_ident = [];
[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = dynare_estimation_init(M_.endo_names, fname, 1, M_, options_, oo_, estim_params_, bayestopt_); [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = dynare_estimation_init(M_.endo_names, fname, 1, M_, options_, oo_, estim_params_, bayestopt_);
%outputting dataset_ is needed for Octave
% set method to compute identification Jacobians (kronflag). Default:0 % set method to compute identification Jacobians (kronflag). Default:0
options_ident = set_default_option(options_ident,'analytic_derivation_mode', options_.analytic_derivation_mode); % if not set by user, inherit default global one options_ident = set_default_option(options_ident,'analytic_derivation_mode', options_.analytic_derivation_mode); % if not set by user, inherit default global one
% 0: efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012) % 0: efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
% 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1) % 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1)
% -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification_numerical_objective.m (previously thet2tau.m) % -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification_numerical_objective.m (previously thet2tau.m)
% -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as if option is set to 0 % -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as with 0
options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting in options_
% initialize persistent variables in prior_draw % initialize persistent variables in prior_draw
if prior_exist if prior_exist
@ -344,14 +340,14 @@ if prior_exist % use estimated_params block
indpcorr = estim_params_.corrx(:,1:2); %values correspond to varexo declaration order, row number corresponds to order in estimated_params indpcorr = estim_params_.corrx(:,1:2); %values correspond to varexo declaration order, row number corresponds to order in estimated_params
end end
totparam_nbr = length(bayestopt_.name); % number of estimated stderr, corr and model parameters as declared in estimated_params totparam_nbr = length(bayestopt_.name); % number of estimated stderr, corr and model parameters as declared in estimated_params
modparam_nbr = estim_params_.np; %number of model parameters as declared in estimated_params modparam_nbr = estim_params_.np; % number of model parameters as declared in estimated_params
stderrparam_nbr = estim_params_.nvx; % nvx is number of stderr parameters stderrparam_nbr = estim_params_.nvx; % number of stderr parameters
corrparam_nbr = estim_params_.ncx; % ncx is number of corr parameters corrparam_nbr = estim_params_.ncx; % number of corr parameters
if estim_params_.nvn || estim_params_.ncn %nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params if estim_params_.nvn || estim_params_.ncn %nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params
error('Identification does not (yet) support measurement errors. Instead, define them explicitly in measurement equations in model definition.') error('Identification does not (yet) support measurement errors. Instead, define them explicitly as varexo and provide measurement equations in the model definition.')
end end
name = cell(totparam_nbr,1); %initialize cell for parameter names name = cell(totparam_nbr,1); %initialize cell for parameter names
name_tex = cell(totparam_nbr,1); name_tex = cell(totparam_nbr,1); %initialize cell for TeX parameter names
for jj=1:totparam_nbr for jj=1:totparam_nbr
if options_.TeX if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_); [param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_);
@ -363,9 +359,9 @@ if prior_exist % use estimated_params block
end end
end end
else % no estimated_params block, choose all model parameters and all stderr parameters, but no corr parameters else % no estimated_params block, choose all model parameters and all stderr parameters, but no corr parameters
indpmodel = 1:M_.param_nbr; %all model parameters indpmodel = 1:M_.param_nbr; %all model parameters
indpstderr = 1:M_.exo_nbr; %all stderr parameters indpstderr = 1:M_.exo_nbr; %all stderr parameters
indpcorr = []; %no corr parameters indpcorr = []; %no corr parameters
stderrparam_nbr = M_.exo_nbr; stderrparam_nbr = M_.exo_nbr;
corrparam_nbr = 0; corrparam_nbr = 0;
modparam_nbr = M_.param_nbr; modparam_nbr = M_.param_nbr;
@ -375,38 +371,38 @@ else % no estimated_params block, choose all model parameters and all stderr par
name_tex = cellfun(@(x) horzcat('$ SE_{', x, '} $'), M_.exo_names, 'UniformOutput', false); name_tex = cellfun(@(x) horzcat('$ SE_{', x, '} $'), M_.exo_names, 'UniformOutput', false);
name_tex = vertcat(name_tex, M_.param_names_tex); name_tex = vertcat(name_tex, M_.param_names_tex);
if ~isequal(M_.H,0) if ~isequal(M_.H,0)
fprintf('\ndynare_identification:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly in measurement equations in the model definition.\n') fprintf('\ndynare_identification:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly as varexo and provide measurement equations in the model definition.\n')
end end
end end
options_ident.name_tex = name_tex; options_ident.name_tex = name_tex;
skipline() fprintf('\n======== Identification Analysis ========\n')
fprintf('======== Identification Analysis ========\n')
if options_ident.order > 1 if options_ident.order > 1
fprintf('Using Pruned State Space System (order=%d)\n',options_ident.order); fprintf('Based on Pruned State Space System (order=%d)\n',options_ident.order);
end end
skipline() skipline()
if totparam_nbr < 2 if totparam_nbr < 2
options_ident.advanced = 0; options_ident.advanced = 0;
disp('There is only one parameter to study for identitification. The advanced option is re-set to 0.') fprintf('There is only one parameter to study for identitification. The advanced option is re-set to 0.\n')
skipline()
end end
% set options_ident dependent ot totparam_nbr % settings dependent on number of parameters
options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,totparam_nbr-1])); options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,totparam_nbr-1]));
options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,totparam_nbr-1]); options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,totparam_nbr-1]);
% In brute force search (ident_bruteforce.m) when advanced=1 this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter % In brute force search (ident_bruteforce.m) when advanced=1 this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter
options_ident = set_default_option(options_ident,'checks_via_subsets',0); options_ident = set_default_option(options_ident,'checks_via_subsets',0);
% 1: uses identification_checks_via_subsets.m to compute problematic parameter combinations % 1: uses identification_checks_via_subsets.m to compute problematic parameter combinations
% 0: uses identification_checks.m to compute problematic parameter combinations [default] % 0: uses identification_checks.m to compute problematic parameter combinations [default]
options_ident = set_default_option(options_ident,'max_dim_subsets_groups',min([4,totparam_nbr-1])); options_ident = set_default_option(options_ident,'max_dim_subsets_groups',min([4,totparam_nbr-1]));
% In identification_checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked % In identification_checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked
options_.options_ident = options_ident; %store identification options into global options
% store identification options
options_.options_ident = options_ident;
store_options_ident = options_ident; store_options_ident = options_ident;
MaxNumberOfBytes = options_.MaxNumberOfBytes; %threshold when to save from memory to files % get some options for quick reference
iload = options_ident.load_ident_files; iload = options_ident.load_ident_files;
SampleSize = options_ident.prior_mc; SampleSize = options_ident.prior_mc;
@ -419,43 +415,37 @@ if iload <=0
% only bounds are specified in estimated_params % only bounds are specified in estimated_params
parameters = 'ML_Starting_value'; parameters = 'ML_Starting_value';
parameters_TeX = 'ML starting value'; parameters_TeX = 'ML starting value';
disp('Testing ML Starting value') fprintf('Testing ML Starting value\n');
else else
% use user-defined option % use user-defined option
switch parameters switch parameters
case 'calibration' case 'calibration'
parameters_TeX = 'Calibration'; parameters_TeX = 'Calibration';
disp('Testing calibration') fprintf('Testing calibration\n');
params(1,:) = get_all_parameters(estim_params_,M_); params(1,:) = get_all_parameters(estim_params_,M_);
case 'posterior_mode' case 'posterior_mode'
parameters_TeX = 'Posterior mode'; parameters_TeX = 'Posterior mode';
disp('Testing posterior mode') fprintf('Testing posterior mode\n');
params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_); params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior_mean' case 'posterior_mean'
parameters_TeX = 'Posterior mean'; parameters_TeX = 'Posterior mean';
disp('Testing posterior mean') fprintf('Testing posterior mean\n');
params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_); params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior_median' case 'posterior_median'
parameters_TeX = 'Posterior median'; parameters_TeX = 'Posterior median';
disp('Testing posterior median') fprintf('Testing posterior median\n');
params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_); params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'prior_mode' case 'prior_mode'
parameters_TeX = 'Prior mode'; parameters_TeX = 'Prior mode';
disp('Testing prior mode') fprintf('Testing prior mode\n');
params(1,:) = bayestopt_.p5(:); params(1,:) = bayestopt_.p5(:);
case 'prior_mean' case 'prior_mean'
parameters_TeX = 'Prior mean'; parameters_TeX = 'Prior mean';
disp('Testing prior mean') fprintf('Testing prior mean\n');
params(1,:) = bayestopt_.p1; params(1,:) = bayestopt_.p1;
otherwise otherwise
disp('The option parameter_set has to be equal to:') fprintf('The option parameter_set has to be equal to: ''calibration'', ''posterior_mode'', ''posterior_mean'', ''posterior_median'', ''prior_mode'' or ''prior_mean''.\n');
disp(' ''calibration'', ') error('IDENTIFICATION: The option ''parameter_set'' has an invalid value');
disp(' ''posterior_mode'', ')
disp(' ''posterior_mean'', ')
disp(' ''posterior_median'', ')
disp(' ''prior_mode'' or')
disp(' ''prior_mean''.')
error('IDENTIFICATION: The option ''parameter_set'' has and invalid value');
end end
end end
else else
@ -463,7 +453,7 @@ if iload <=0
params = [sqrt(diag(M_.Sigma_e))', M_.params']; params = [sqrt(diag(M_.Sigma_e))', M_.params'];
parameters = 'Current_params'; parameters = 'Current_params';
parameters_TeX = 'Current parameter values'; parameters_TeX = 'Current parameter values';
disp('Testing all current stderr and model parameter values') fprintf('Testing all current stderr and model parameter values\n');
end end
options_ident.tittxt = parameters; %title text for graphs and figures options_ident.tittxt = parameters; %title text for graphs and figures
% perform identification analysis for single point % perform identification analysis for single point
@ -471,43 +461,13 @@ if iload <=0
identification_analysis(params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables identification_analysis(params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables
if info(1)~=0 if info(1)~=0
% there are errors in the solution algorithm % there are errors in the solution algorithm
skipline() message = get_error_message(info,0,options_);
disp('----------- ') fprintf('-----------\n');
disp('Parameter error:') fprintf('The model does not solve for %s (info = %d: %s)\n', parameters, info(1), message);
disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]), fprintf('-----------\n');
skipline()
if info(1)==1
disp('info==1 %! The model doesn''t determine the current variables uniquely.')
elseif info(1)==2
disp('info==2 %! MJDGGES returned an error code.')
elseif info(1)==3
disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
elseif info(1)==4
disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
elseif info(1)==5
disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
elseif info(1)==6
disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
elseif info(1)==19
disp('info==19 %! The steadystate routine has thrown an exception (inconsistent deep parameters). ')
elseif info(1)==20
disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
elseif info(1)==21
disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
elseif info(1)==22
disp('info==22 %! The steady has NaNs. ')
elseif info(1)==23
disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
elseif info(1)==24
disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
elseif info(1)==30
disp('info==30 %! Ergodic variance can''t be computed. ')
end
disp('----------- ')
skipline()
if any(bayestopt_.pshape) if any(bayestopt_.pshape)
% if there are errors in the solution algorithm, try to sample a different point from the prior % if there are errors in the solution algorithm, try to sample a different point from the prior
disp('Try sampling up to 50 parameter sets from the prior.') fprintf('Try sampling up to 50 parameter sets from the prior.\n');
kk=0; kk=0;
while kk<50 && info(1) while kk<50 && info(1)
kk=kk+1; kk=kk+1;
@ -515,53 +475,50 @@ if iload <=0
options_ident.tittxt = 'Random_prior_params'; %title text for graphs and figures options_ident.tittxt = 'Random_prior_params'; %title text for graphs and figures
% perform identification analysis % perform identification analysis
[ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, options_ident] = ... [ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, options_ident] = ...
identification_analysis(params,indpmodel,indpstderr,indpcorr,options_ident,dataset_info, prior_exist, 1); identification_analysis(params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
end end
end end
if info(1) if info(1)
skipline() fprintf('\n-----------\n');
disp('----------- ') fprintf('Identification stopped:\n');
disp('Identification stopped:')
if any(bayestopt_.pshape) if any(bayestopt_.pshape)
disp('The model did not solve for any of 50 attempts of random samples from the prior') fprintf('The model did not solve for any of 50 attempts of random samples from the prior\n');
end end
disp('----------- ') fprintf('-----------\n');
skipline()
return return
else else
% found a (random) point that solves the model % found a (random) point that solves the model
disp('Found a random draw from the priors that solves the model.') fprintf('Found a random draw from the priors that solves the model:\n');
disp(params) disp(params);
disp('Identification now continues for this draw.'); fprintf('Identification now continues for this draw.');
parameters = 'Random_prior_params'; parameters = 'Random_prior_params';
parameters_TeX = 'Random prior parameter draw'; parameters_TeX = 'Random prior parameter draw';
end end
end end
ide_hess_point.params = params; ide_hess_point.params = params;
% save all output into identification folder % save all output into identification folder
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point','store_options_ident'); save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
save([IdentifDirectoryName '/' fname '_' parameters '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point','store_options_ident'); save([IdentifDirectoryName '/' fname '_' parameters '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
% display results of identification analysis % display results of identification analysis
disp_identification(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident); disp_identification(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
if ~options_ident.no_identification_strength && ~options_.nograph if ~options_ident.no_identification_strength && ~options_.nograph
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs % plot (i) identification strength and sensitivity measure based on the moment information matrix and (ii) plot advanced analysis graphs
plot_identification(params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, parameters_TeX, name_tex); plot_identification(params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, parameters_TeX, name_tex);
end end
if SampleSize > 1 if SampleSize > 1
% initializations for Monte Carlo Analysis % initializations for Monte Carlo Analysis
skipline() fprintf('\nMonte Carlo Testing\n');
disp('Monte Carlo Testing')
h = dyn_waitbar(0,'Monte Carlo identification checks ...'); h = dyn_waitbar(0,'Monte Carlo identification checks ...');
iteration = 0; % initialize counter for admissable draws iteration = 0; % initialize counter for admissable draws
run_index = 0; % initialize counter for admissable draws after saving previous draws to file(s) run_index = 0; % initialize counter for admissable draws after saving previous draws to file(s)
file_index = 0; % initialize counter for files (if MaxNumberOfBytes is reached, we store results in files) file_index = 0; % initialize counter for files (if options_.MaxNumberOfBytes is reached, we store results in files)
options_MC = options_ident; %store options structure for Monte Carlo analysis options_MC = options_ident; %store options structure for Monte Carlo analysis
options_MC.advanced = 0; %do not run advanced checking in a Monte Carlo analysis options_MC.advanced = 0; %do not run advanced checking in a Monte Carlo analysis
options_ident.checks_via_subsets = 0; % for Monte Carlo analysis currently only identification_checks and not identification_checks_via_subsets is supported options_ident.checks_via_subsets = 0; % for Monte Carlo analysis currently only identification_checks and not identification_checks_via_subsets is supported
else else
iteration = 1; % iteration equals SampleSize and we are finished iteration = 1; % iteration equals SampleSize and we are finished
pdraws = []; % to have output object otherwise map_ident may crash pdraws = []; % to have output object otherwise map_ident.m may crash
end end
while iteration < SampleSize while iteration < SampleSize
if external_sample if external_sample
@ -576,78 +533,84 @@ if iload <=0
if iteration==0 && info(1)==0 % preallocate storage in the first admissable run if iteration==0 && info(1)==0 % preallocate storage in the first admissable run
delete([IdentifDirectoryName '/' fname '_identif_*.mat']) % delete previously saved results delete([IdentifDirectoryName '/' fname '_identif_*.mat']) % delete previously saved results
MAX_RUNS_BEFORE_SAVE_TO_FILE = min(SampleSize,ceil(MaxNumberOfBytes/(size(ide_reducedform.si_dREDUCEDFORM,1)*totparam_nbr)/8)); % set how many runs can be stored before we save to files MAX_RUNS_BEFORE_SAVE_TO_FILE = min(SampleSize,ceil(options_.MaxNumberOfBytes/(size(ide_reducedform.si_dREDUCEDFORM,1)*totparam_nbr)/8)); % set how many runs can be stored before we save to files
pdraws = zeros(SampleSize,totparam_nbr); % preallocate storage for draws in each row pdraws = zeros(SampleSize,totparam_nbr); % preallocate storage for draws in each row
% preallocate storage for steady state and dynamic model derivatives % preallocate storage for dynamic model
STO_si_dDYNAMIC = zeros([size(ide_dynamic.si_dDYNAMIC,1),modparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]); STO_si_dDYNAMIC = zeros([size(ide_dynamic.si_dDYNAMIC, 1), modparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
STO_DYNAMIC = zeros(size(ide_dynamic.DYNAMIC,1),SampleSize); STO_DYNAMIC = zeros(size(ide_dynamic.DYNAMIC, 1), SampleSize);
IDE_DYNAMIC.ind_dDYNAMIC = ide_dynamic.ind_dDYNAMIC; IDE_DYNAMIC.ind_dDYNAMIC = ide_dynamic.ind_dDYNAMIC;
IDE_DYNAMIC.in0 = zeros(SampleSize,modparam_nbr); IDE_DYNAMIC.in0 = zeros(SampleSize, modparam_nbr);
IDE_DYNAMIC.ind0 = zeros(SampleSize,modparam_nbr); IDE_DYNAMIC.ind0 = zeros(SampleSize, modparam_nbr);
IDE_DYNAMIC.jweak = zeros(SampleSize,modparam_nbr); IDE_DYNAMIC.jweak = zeros(SampleSize, modparam_nbr);
IDE_DYNAMIC.jweak_pair = zeros(SampleSize,modparam_nbr*(modparam_nbr+1)/2); IDE_DYNAMIC.jweak_pair = zeros(SampleSize, modparam_nbr*(modparam_nbr+1)/2);
IDE_DYNAMIC.cond = zeros(SampleSize,1); IDE_DYNAMIC.cond = zeros(SampleSize, 1);
IDE_DYNAMIC.Mco = zeros(SampleSize,modparam_nbr); IDE_DYNAMIC.Mco = zeros(SampleSize, modparam_nbr);
% preallocate storage for reduced form % preallocate storage for reduced form
if ~options_MC.no_identification_reducedform if ~options_MC.no_identification_reducedform
STO_si_dREDUCEDFORM = zeros([size(ide_reducedform.si_dREDUCEDFORM,1),totparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]); STO_si_dREDUCEDFORM = zeros([size(ide_reducedform.si_dREDUCEDFORM, 1), totparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
STO_REDUCEDFORM = zeros(size(ide_reducedform.REDUCEDFORM,1),SampleSize); STO_REDUCEDFORM = zeros(size(ide_reducedform.REDUCEDFORM, 1), SampleSize);
IDE_REDUCEDFORM.ind_dREDUCEDFORM = ide_reducedform.ind_dREDUCEDFORM; IDE_REDUCEDFORM.ind_dREDUCEDFORM = ide_reducedform.ind_dREDUCEDFORM;
IDE_REDUCEDFORM.in0 = zeros(SampleSize,1); IDE_REDUCEDFORM.in0 = zeros(SampleSize, 1);
IDE_REDUCEDFORM.ind0 = zeros(SampleSize,totparam_nbr); IDE_REDUCEDFORM.ind0 = zeros(SampleSize, totparam_nbr);
IDE_REDUCEDFORM.jweak = zeros(SampleSize,totparam_nbr); IDE_REDUCEDFORM.jweak = zeros(SampleSize, totparam_nbr);
IDE_REDUCEDFORM.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2); IDE_REDUCEDFORM.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
IDE_REDUCEDFORM.cond = zeros(SampleSize,1); IDE_REDUCEDFORM.cond = zeros(SampleSize, 1);
IDE_REDUCEDFORM.Mco = zeros(SampleSize,totparam_nbr); IDE_REDUCEDFORM.Mco = zeros(SampleSize, totparam_nbr);
else else
IDE_REDUCEDFORM = {}; STO_si_dREDUCEDFORM = {};
STO_REDUCEDFORM = {};
IDE_REDUCEDFORM = {};
end end
% preallocate storage for moments % preallocate storage for moments
if ~options_MC.no_identification_moments if ~options_MC.no_identification_moments
STO_si_dMOMENTS = zeros([size(ide_moments.si_dMOMENTS,1),totparam_nbr,MAX_RUNS_BEFORE_SAVE_TO_FILE]); STO_si_dMOMENTS = zeros([size(ide_moments.si_dMOMENTS, 1), totparam_nbr, MAX_RUNS_BEFORE_SAVE_TO_FILE]);
STO_MOMENTS = zeros(size(ide_moments.MOMENTS,1),SampleSize); STO_MOMENTS = zeros(size(ide_moments.MOMENTS, 1), SampleSize);
IDE_MOMENTS.ind_dMOMENTS = ide_moments.ind_dMOMENTS; IDE_MOMENTS.ind_dMOMENTS = ide_moments.ind_dMOMENTS;
IDE_MOMENTS.in0 = zeros(SampleSize,1); IDE_MOMENTS.in0 = zeros(SampleSize, 1);
IDE_MOMENTS.ind0 = zeros(SampleSize,totparam_nbr); IDE_MOMENTS.ind0 = zeros(SampleSize, totparam_nbr);
IDE_MOMENTS.jweak = zeros(SampleSize,totparam_nbr); IDE_MOMENTS.jweak = zeros(SampleSize, totparam_nbr);
IDE_MOMENTS.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2); IDE_MOMENTS.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
IDE_MOMENTS.cond = zeros(SampleSize,1); IDE_MOMENTS.cond = zeros(SampleSize, 1);
IDE_MOMENTS.Mco = zeros(SampleSize,totparam_nbr); IDE_MOMENTS.Mco = zeros(SampleSize, totparam_nbr);
IDE_MOMENTS.S = zeros(SampleSize,min(8,totparam_nbr)); IDE_MOMENTS.S = zeros(SampleSize, min(8, totparam_nbr));
IDE_MOMENTS.V = zeros(SampleSize,totparam_nbr,min(8,totparam_nbr)); IDE_MOMENTS.V = zeros(SampleSize, totparam_nbr, min(8, totparam_nbr));
else else
IDE_MOMENTS = {}; STO_si_dMOMENTS = {};
STO_MOMENTS = {};
IDE_MOMENTS = {};
end end
% preallocate storage for spectrum % preallocate storage for spectrum
if ~options_MC.no_identification_spectrum if ~options_MC.no_identification_spectrum
STO_dSPECTRUM = zeros([size(ide_spectrum.dSPECTRUM,1),size(ide_spectrum.dSPECTRUM,2), MAX_RUNS_BEFORE_SAVE_TO_FILE]); STO_dSPECTRUM = zeros([size(ide_spectrum.dSPECTRUM, 1), size(ide_spectrum.dSPECTRUM, 2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
IDE_SPECTRUM.ind_dSPECTRUM = ide_spectrum.ind_dSPECTRUM; IDE_SPECTRUM.ind_dSPECTRUM = ide_spectrum.ind_dSPECTRUM;
IDE_SPECTRUM.in0 = zeros(SampleSize,1); IDE_SPECTRUM.in0 = zeros(SampleSize, 1);
IDE_SPECTRUM.ind0 = zeros(SampleSize,totparam_nbr); IDE_SPECTRUM.ind0 = zeros(SampleSize, totparam_nbr);
IDE_SPECTRUM.jweak = zeros(SampleSize,totparam_nbr); IDE_SPECTRUM.jweak = zeros(SampleSize, totparam_nbr);
IDE_SPECTRUM.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2); IDE_SPECTRUM.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
IDE_SPECTRUM.cond = zeros(SampleSize,1); IDE_SPECTRUM.cond = zeros(SampleSize, 1);
IDE_SPECTRUM.Mco = zeros(SampleSize,totparam_nbr); IDE_SPECTRUM.Mco = zeros(SampleSize, totparam_nbr);
else else
IDE_SPECTRUM = {}; STO_dSPECTRUM = {};
IDE_SPECTRUM = {};
end end
% preallocate storage for minimal system % preallocate storage for minimal system
if ~options_MC.no_identification_minimal if ~options_MC.no_identification_minimal
STO_dMINIMAL = zeros([size(ide_minimal.dMINIMAL,1),size(ide_minimal.dMINIMAL,2), MAX_RUNS_BEFORE_SAVE_TO_FILE]); STO_dMINIMAL = zeros([size(ide_minimal.dMINIMAL, 1), size(ide_minimal.dMINIMAL, 2), MAX_RUNS_BEFORE_SAVE_TO_FILE]);
IDE_MINIMAL.ind_dMINIMAL = ide_minimal.ind_dMINIMAL; IDE_MINIMAL.ind_dMINIMAL = ide_minimal.ind_dMINIMAL;
IDE_MINIMAL.in0 = zeros(SampleSize,1); IDE_MINIMAL.in0 = zeros(SampleSize, 1);
IDE_MINIMAL.ind0 = zeros(SampleSize,totparam_nbr); IDE_MINIMAL.ind0 = zeros(SampleSize, totparam_nbr);
IDE_MINIMAL.jweak = zeros(SampleSize,totparam_nbr); IDE_MINIMAL.jweak = zeros(SampleSize, totparam_nbr);
IDE_MINIMAL.jweak_pair = zeros(SampleSize,totparam_nbr*(totparam_nbr+1)/2); IDE_MINIMAL.jweak_pair = zeros(SampleSize, totparam_nbr*(totparam_nbr+1)/2);
IDE_MINIMAL.cond = zeros(SampleSize,1); IDE_MINIMAL.cond = zeros(SampleSize, 1);
IDE_MINIMAL.Mco = zeros(SampleSize,totparam_nbr); IDE_MINIMAL.Mco = zeros(SampleSize, totparam_nbr);
else else
IDE_MINIMAL = {}; STO_dMINIMAL = {};
IDE_MINIMAL = {};
end end
end end
@ -666,10 +629,10 @@ if iload <=0
IDE_DYNAMIC.jweak_pair(iteration,:) = ide_dynamic.jweak_pair; IDE_DYNAMIC.jweak_pair(iteration,:) = ide_dynamic.jweak_pair;
IDE_DYNAMIC.Mco(iteration,:) = ide_dynamic.Mco; IDE_DYNAMIC.Mco(iteration,:) = ide_dynamic.Mco;
% store results for reduced form model solution % store results for reduced form
if ~options_MC.no_identification_reducedform if ~options_MC.no_identification_reducedform
STO_REDUCEDFORM(:,iteration) = ide_reducedform.REDUCEDFORM; STO_REDUCEDFORM(:,iteration) = ide_reducedform.REDUCEDFORM;
STO_si_dREDUCEDFORM(:,:,run_index) = ide_reducedform.si_dREDUCEDFORM; STO_si_dREDUCEDFORM(:,:,run_index) = ide_reducedform.si_dREDUCEDFORM;
IDE_REDUCEDFORM.cond(iteration,1) = ide_reducedform.cond; IDE_REDUCEDFORM.cond(iteration,1) = ide_reducedform.cond;
IDE_REDUCEDFORM.ino(iteration,1) = ide_reducedform.ino; IDE_REDUCEDFORM.ino(iteration,1) = ide_reducedform.ino;
IDE_REDUCEDFORM.ind0(iteration,:) = ide_reducedform.ind0; IDE_REDUCEDFORM.ind0(iteration,:) = ide_reducedform.ind0;
@ -717,7 +680,7 @@ if iload <=0
% save results to file: either to avoid running into memory issues, i.e. (run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE) or if finished (iteration==SampleSize) % save results to file: either to avoid running into memory issues, i.e. (run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE) or if finished (iteration==SampleSize)
if run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE || iteration==SampleSize if run_index==MAX_RUNS_BEFORE_SAVE_TO_FILE || iteration==SampleSize
file_index = file_index + 1; file_index = file_index + 1;
if run_index<MAX_RUNS_BEFORE_SAVE_TO_FILE if run_index < MAX_RUNS_BEFORE_SAVE_TO_FILE
%we are finished (iteration == SampleSize), so get rid of additional storage %we are finished (iteration == SampleSize), so get rid of additional storage
STO_si_dDYNAMIC = STO_si_dDYNAMIC(:,:,1:run_index); STO_si_dDYNAMIC = STO_si_dDYNAMIC(:,:,1:run_index);
if ~options_MC.no_identification_reducedform if ~options_MC.no_identification_reducedform
@ -754,7 +717,7 @@ if iload <=0
run_index = 0; % reset index run_index = 0; % reset index
end end
if SampleSize > 1 if SampleSize > 1
dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)]) dyn_waitbar(iteration/SampleSize, h, ['MC identification checks ', int2str(iteration), '/', int2str(SampleSize)]);
end end
end end
end end
@ -769,10 +732,10 @@ if iload <=0
normalize_STO_MOMENTS = std(STO_MOMENTS,0,2); normalize_STO_MOMENTS = std(STO_MOMENTS,0,2);
end end
if ~options_MC.no_identification_minimal if ~options_MC.no_identification_minimal
normalize_STO_MINIMAL = 1; %not yet used normalize_STO_MINIMAL = 1; %not used (yet)
end end
if ~options_MC.no_identification_spectrum if ~options_MC.no_identification_spectrum
normalize_STO_SPECTRUM = 1; %not yet used normalize_STO_SPECTRUM = 1; %not used (yet)
end end
normaliz1 = std(pdraws); normaliz1 = std(pdraws);
iter = 0; iter = 0;
@ -854,11 +817,11 @@ end
%% if dynare_identification is called as it own function (not through identification command) and if we load files %% if dynare_identification is called as it own function (not through identification command) and if we load files
if nargout>3 && iload if nargout>3 && iload
filnam = dir([IdentifDirectoryName '/' fname '_identif_*.mat']); filnam = dir([IdentifDirectoryName '/' fname '_identif_*.mat']);
STO_si_dDYNAMIC = []; STO_si_dDYNAMIC = [];
STO_si_dREDUCEDFORM=[]; STO_si_dREDUCEDFORM = [];
STO_si_dMOMENTS = []; STO_si_dMOMENTS = [];
STO_dSPECTRUM = []; STO_dSPECTRUM = [];
STO_dMINIMAL = []; STO_dMINIMAL = [];
for j=1:length(filnam) for j=1:length(filnam)
load([IdentifDirectoryName '/' fname '_identif_',int2str(j),'.mat']); load([IdentifDirectoryName '/' fname '_identif_',int2str(j),'.mat']);
STO_si_dDYNAMIC = cat(3,STO_si_dDYNAMIC, STO_si_dDYNAMIC(:,abs(iload),:)); STO_si_dDYNAMIC = cat(3,STO_si_dDYNAMIC, STO_si_dDYNAMIC(:,abs(iload),:));
@ -879,18 +842,17 @@ end
if iload if iload
%if previous analysis is loaded %if previous analysis is loaded
disp(['Testing ',parameters]) fprintf(['Testing %s\n',parameters]);
disp_identification(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident); disp_identification(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
if ~options_.nograph if ~options_.nograph
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs % plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, [],name_tex); plot_identification(ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, IdentifDirectoryName, [], name_tex);
end end
end end
%displaying and plotting of results for MC sample %displaying and plotting of results for MC sample
if SampleSize > 1 if SampleSize > 1
fprintf('\n') fprintf('\nTesting MC sample\n');
disp('Testing MC sample')
%print results to console but make sure advanced=0 %print results to console but make sure advanced=0
advanced0 = options_ident.advanced; advanced0 = options_ident.advanced;
options_ident.advanced = 0; options_ident.advanced = 0;
@ -909,11 +871,9 @@ if SampleSize > 1
store_nodisplay = options_.nodisplay; store_nodisplay = options_.nodisplay;
options_.nodisplay = 1; options_.nodisplay = 1;
% HIGHEST CONDITION NUMBER % HIGHEST CONDITION NUMBER
[~, jmax] = max(IDE_MOMENTS.cond); [~, jmax] = max(IDE_MOMENTS.cond);
fprintf('\n')
tittxt = 'Draw with HIGHEST condition number'; tittxt = 'Draw with HIGHEST condition number';
fprintf('\n') fprintf('\nTesting %s.\n',tittxt);
disp(['Testing ',tittxt, '.']),
if ~iload if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_max, ide_spectrum_max, ide_minimal_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, derivatives_info_max, info_max, options_ident] = ... [ide_moments_max, ide_spectrum_max, ide_minimal_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, derivatives_info_max, info_max, options_ident] = ...
@ -930,10 +890,8 @@ if SampleSize > 1
% SMALLEST condition number % SMALLEST condition number
[~, jmin] = min(IDE_MOMENTS.cond); [~, jmin] = min(IDE_MOMENTS.cond);
fprintf('\n')
tittxt = 'Draw with SMALLEST condition number'; tittxt = 'Draw with SMALLEST condition number';
fprintf('\n') fprintf('Testing %s.\n',tittxt);
disp(['Testing ',tittxt, '.']),
if ~iload if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, derivatives_info_min, info_min, options_ident] = ... [ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, derivatives_info_min, info_min, options_ident] = ...
@ -954,9 +912,8 @@ if SampleSize > 1
store_nodisplay = options_.nodisplay; store_nodisplay = options_.nodisplay;
options_.nodisplay = 1; options_.nodisplay = 1;
for j=1:length(jcrit) for j=1:length(jcrit)
tittxt = ['Rank deficient draw n. ',int2str(j)]; tittxt = ['Rank deficient draw nr. ',int2str(j)];
fprintf('\n') fprintf('\nTesting %s.\n',tittxt);
disp(['Testing ',tittxt, '.']),
if ~iload if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), derivatives_info_(j), info_resolve, options_ident] = ... [ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), derivatives_info_(j), info_resolve, options_ident] = ...
@ -987,8 +944,6 @@ else
warning on warning on
end end
skipline() fprintf('\n==== Identification analysis completed ====\n\n')
disp('==== Identification analysis completed ====')
skipline(2)
options_ = store_options_; %restore options set options_ = store_options_; %restore options set

2
matlab/fjaco.m
View File

@ -10,7 +10,7 @@ function fjac = fjaco(f,x,varargin)
% OUTPUT % OUTPUT
% fjac : finite difference Jacobian % fjac : finite difference Jacobian
% %
% Copyright (C) 2010-2017,2019 Dynare Team % Copyright (C) 2010-2017,2019-2020 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %

56
matlab/get_identification_jacobians.m
View File

@ -1,7 +1,7 @@
function [E_y, dE_y, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs) function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs)
% function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs) % function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M, oo, options, options_ident, indpmodel, indpstderr, indpcorr, indvobs)
% previously getJJ.m % previously getJJ.m in Dynare 4.5
% % Sets up the Jacobians needed for identification analysis % Sets up the Jacobians needed for identification analysis
% ========================================================================= % =========================================================================
% INPUTS % INPUTS
% estim_params: [structure] storing the estimation information % estim_params: [structure] storing the estimation information
@ -27,46 +27,50 @@ function [E_y, dE_y, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOM
% %
% MEAN [endo_nbr by 1], in DR order. Expectation of all model variables % MEAN [endo_nbr by 1], in DR order. Expectation of all model variables
% * order==1: corresponds to steady state % * order==1: corresponds to steady state
% * order==2: computed from pruned state space system (as in e.g. Andreasen, Fernandez-Villaverde, Rubio-Ramirez, 2018) % * order==2|3: corresponds to mean computed from pruned state space system (as in Andreasen, Fernandez-Villaverde, Rubio-Ramirez, 2018)
% dMEAN [endo_nbr by totparam_nbr], in DR Order, Jacobian (wrt all params) of MEAN % dMEAN [endo_nbr by totparam_nbr], in DR Order, Jacobian (wrt all params) of MEAN
% %
% REDUCEDFORM [rowredform_nbr by 1] in DR order. Steady state and reduced-form model solution matrices for all model variables % REDUCEDFORM [rowredform_nbr by 1] in DR order. Steady state and reduced-form model solution matrices for all model variables
% * order==1: [Yss' vec(dghx)' vech(dghu*Sigma_e*dghu')']', % * order==1: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')']',
% where rowredform_nbr = endo_nbr+endo_nbr*nspred+endo_nbr*(endo_nbr+1)/2 % where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2)
% * order==2: [Yss' vec(dghx)' vech(dghu*Sigma_e*dghu')' vec(dghxx)' vec(dghxu)' vec(dghuu)' vec(dghs2)']', % * order==2: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')' vec(ghxx)' vec(ghxu)' vec(ghuu)' vec(ghs2)']',
% where rowredform_nbr = endo_nbr+endo_nbr*nspred+endo_nbr*(endo_nbr+1)/2+endo_nbr*nspred^2*endo_nbr*nspred*exo_nr+endo_nbr*exo_nbr^2+endo_nbr % where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2+nspred^2+nspred*exo_nr+exo_nbr^2+1)
% dREDUCEDFORM: [rowrf_nbr by totparam_nbr] in DR order, Jacobian (wrt all params) of REDUCEDFORM % * order==3: [Yss' vec(ghx)' vech(ghu*Sigma_e*ghu')' vec(ghxx)' vec(ghxu)' vec(ghuu)' vec(ghs2)' vec(ghxxx)' vec(ghxxu)' vec(ghxuu)' vec(ghuuu)' vec(ghxss)' vec(ghuss)']',
% where rowredform_nbr = endo_nbr*(1+nspred+(endo_nbr+1)/2+nspred^2+nspred*exo_nr+exo_nbr^2+1+nspred^3+nspred^2*exo_nbr+nspred*exo_nbr^2+exo_nbr^3+nspred+exo_nbr)
% dREDUCEDFORM: [rowredform_nbr by totparam_nbr] in DR order, Jacobian (wrt all params) of REDUCEDFORM
% * order==1: corresponds to Iskrev (2010)'s J_2 matrix % * order==1: corresponds to Iskrev (2010)'s J_2 matrix
% * order==2: corresponds to Mutschler (2015)'s J matrix % * order==2: corresponds to Mutschler (2015)'s J matrix
% %
% DYNAMIC [rowdyn_nbr by 1] in declaration order. Steady state and dynamic model derivatives for all model variables % DYNAMIC [rowdyn_nbr by 1] in declaration order. Steady state and dynamic model derivatives for all model variables
% * order==1: [ys' vec(g1)']', rowdyn_nbr=endo_nbr+length(g1) % * order==1: [ys' vec(g1)']', rowdyn_nbr=endo_nbr+length(g1)
% * order==2: [ys' vec(g1)' vec(g2)']', rowdyn_nbr=endo_nbr+length(g1)+length(g2) % * order==2: [ys' vec(g1)' vec(g2)']', rowdyn_nbr=endo_nbr+length(g1)+length(g2)
% * order==3: [ys' vec(g1)' vec(g2)' vec(g3)']', rowdyn_nbr=endo_nbr+length(g1)+length(g2)+length(g3)
% dDYNAMIC [rowdyn_nbr by modparam_nbr] in declaration order. Jacobian (wrt model parameters) of DYNAMIC % dDYNAMIC [rowdyn_nbr by modparam_nbr] in declaration order. Jacobian (wrt model parameters) of DYNAMIC
% * order==1: corresponds to Ratto and Iskrev (2011)'s J_\Gamma matrix (or LRE) % * order==1: corresponds to Ratto and Iskrev (2011)'s J_\Gamma matrix (or LRE)
% * order==2: additionally includes derivatives of dynamic hessian
% %
% MOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by 1] in DR order. First two theoretical moments for VAROBS variables, i.e. % MOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by 1] in DR order. First two theoretical moments for VAROBS variables, i.e.
% [E[yobs]' vech(E[yobs*yobs'])' vec(E[yobs*yobs(-1)'])' ... vec(E[yobs*yobs(-nlag)'])'] % [E[varobs]' vech(E[varobs*varobs'])' vec(E[varobs*varobs(-1)'])' ... vec(E[varobs*varobs(-nlag)'])']
% dMOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by totparam_nbr] in DR order. Jacobian (wrt all params) of MOMENTS % dMOMENTS: [obs_nbr+obs_nbr*(obs_nbr+1)/2+nlags*obs_nbr^2 by totparam_nbr] in DR order. Jacobian (wrt all params) of MOMENTS
% * order==1: corresponds to Iskrev (2010)'s J matrix % * order==1: corresponds to Iskrev (2010)'s J matrix
% * order==2: corresponds to Mutschler (2015)'s \bar{M}_2 matrix, i.e. theoretical moments from the pruned state space system % * order==2: corresponds to Mutschler (2015)'s \bar{M}_2 matrix, i.e. theoretical moments from the pruned state space system
% %
% dSPECTRUM: [totparam_nbr by totparam_nbr] in DR order. Gram matrix of Jacobian (wrt all params) of spectral density for VAROBS variables, where % dSPECTRUM: [totparam_nbr by totparam_nbr] in DR order. Gram matrix of Jacobian (wrt all params) of spectral density for VAROBS variables, where
% spectral density at frequency w: f(w) = (2*pi)^(-1)*H(exp(-i*w))*E[xi*xi']*ctranspose(H(exp(-i*w)) with H being the Transfer function % spectral density at frequency w: f(w) = (2*pi)^(-1)*H(exp(-i*w))*E[Inov*Inov']*ctranspose(H(exp(-i*w)) with H being the Transfer function
% dSPECTRUM = int_{-\pi}^\pi transpose(df(w)/dp')*(df(w)/dp') dw % dSPECTRUM = int_{-\pi}^\pi transpose(df(w)/dp')*(df(w)/dp') dw
% * order==1: corresponds to Qu and Tkachenko (2012)'s G matrix, where xi and H are computed from linear state space system % * order==1: corresponds to Qu and Tkachenko (2012)'s G matrix, where Inov and H are computed from linear state space system
% * order==2: corresponds to Mutschler (2015)'s G_2 matrix, where xi and H are computed from pruned state space system % * order==2: corresponds to Mutschler (2015)'s G_2 matrix, where Inov and H are computed from second-order pruned state space system
% * order==3: Inov and H are computed from third-order pruned state space system
% %
% dMINIMAL: [obs_nbr+minx^2+minx*exo_nbr+obs_nbr*minx+obs_nbr*exo_nbr+exo_nbr*(exo_nbr+1)/2 by totparam_nbr+minx^2+exo_nbr^2] % dMINIMAL: [obs_nbr+minx_nbr^2+minx_nbr*exo_nbr+obs_nbr*minx_nbr+obs_nbr*exo_nbr+exo_nbr*(exo_nbr+1)/2 by totparam_nbr+minx_nbr^2+exo_nbr^2]
% Jacobian (wrt all params, and similarity_transformation_matrices (T and U)) of observational equivalent minimal ABCD system, % Jacobian (wrt all params, and similarity_transformation_matrices (T and U)) of observational equivalent minimal ABCD system,
% corresponds to Komunjer and Ng (2011)'s Deltabar matrix, where % corresponds to Komunjer and Ng (2011)'s Deltabar matrix, where
% MINIMAL = [vec(E[yobs]' vec(minA)' vec(minB)' vec(minC)' vec(minD)' vech(Sigma_e)']' % MINIMAL = [vec(E[varobs]' vec(minA)' vec(minB)' vec(minC)' vec(minD)' vech(Sigma_e)']'
% * order==1: E[yobs] is equal to steady state % minA, minB, minC and minD is the minimal state space system computed in get_minimal_state_representation
% * order==2: E[yobs] is computed from the pruned state space system (second-order accurate), as noted in section 5 of Komunjer and Ng (2011) % * order==1: E[varobs] is equal to steady state
% * order==2|3: E[varobs] is computed from the pruned state space system (second|third-order accurate), as noted in section 5 of Komunjer and Ng (2011)
% %
% derivatives_info [structure] for use in dsge_likelihood to compute Hessian analytically. % derivatives_info [structure] for use in dsge_likelihood to compute Hessian analytically. Only used at order==1.
% Contains dA, dB, and d(B*Sigma_e*B'), where A and B are from Kalman filter. Only used at order==1. % Contains dA, dB, and d(B*Sigma_e*B'), where A and B are Kalman filter transition matrice.
% %
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function is called by % This function is called by
@ -220,7 +224,7 @@ options.options_ident.indpmodel = indpmodel;
options.options_ident.indpstderr = indpstderr; options.options_ident.indpstderr = indpstderr;
options.options_ident.indpcorr = indpcorr; options.options_ident.indpcorr = indpcorr;
oo.dr = pruned_state_space_system(M, options, oo.dr); oo.dr = pruned_state_space_system(M, options, oo.dr);
E_y = oo.dr.pruned.E_y; dE_y = oo.dr.pruned.dE_y; MEAN = oo.dr.pruned.E_y; dMEAN = oo.dr.pruned.dE_y;
A = oo.dr.pruned.A; dA = oo.dr.pruned.dA; A = oo.dr.pruned.A; dA = oo.dr.pruned.dA;
B = oo.dr.pruned.B; dB = oo.dr.pruned.dB; B = oo.dr.pruned.B; dB = oo.dr.pruned.dB;
C = oo.dr.pruned.C; dC = oo.dr.pruned.dC; C = oo.dr.pruned.C; dC = oo.dr.pruned.dC;
@ -247,17 +251,17 @@ end
% yhat = C*zhat(-1) + D*xi, where yhat = y - E(y) % yhat = C*zhat(-1) + D*xi, where yhat = y - E(y)
if ~no_identification_moments if ~no_identification_moments
MOMENTS = identification_numerical_objective(para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1] MOMENTS = identification_numerical_objective(para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
MOMENTS = [E_y; MOMENTS]; MOMENTS = [MEAN; MOMENTS];
if kronflag == -1 if kronflag == -1
%numerical derivative of autocovariogram %numerical derivative of autocovariogram
dMOMENTS = fjaco(str2func('identification_numerical_objective'), para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1] dMOMENTS = fjaco(str2func('identification_numerical_objective'), para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
M.params = params0; %make sure values are set back M.params = params0; %make sure values are set back
M.Sigma_e = Sigma_e0; %make sure values are set back M.Sigma_e = Sigma_e0; %make sure values are set back
M.Correlation_matrix = Corr_e0 ; %make sure values are set back M.Correlation_matrix = Corr_e0 ; %make sure values are set back
dMOMENTS = [dE_y; dMOMENTS]; %add Jacobian of steady state of VAROBS variables dMOMENTS = [dMEAN; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
else else
dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr); dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr);
dMOMENTS(1:obs_nbr,:) = dE_y; %add Jacobian of first moments of VAROBS variables dMOMENTS(1:obs_nbr,:) = dMEAN; %add Jacobian of first moments of VAROBS variables
% Denote Ezz0 = E[zhat*zhat'], then the following Lyapunov equation defines the autocovariagram: Ezz0 -A*Ezz0*A' = B*Sig_xi*B' = Om_z % Denote Ezz0 = E[zhat*zhat'], then the following Lyapunov equation defines the autocovariagram: Ezz0 -A*Ezz0*A' = B*Sig_xi*B' = Om_z
[Ezz0,u] = lyapunov_symm(A, Om_z, options.lyapunov_fixed_point_tol, options.qz_criterium, options.lyapunov_complex_threshold, 1, options.debug); [Ezz0,u] = lyapunov_symm(A, Om_z, options.lyapunov_fixed_point_tol, options.qz_criterium, options.lyapunov_complex_threshold, 1, options.debug);
stationary_vars = (1:length(indvobs))'; stationary_vars = (1:length(indvobs))';
@ -426,7 +430,7 @@ if ~no_identification_spectrum
end end
end end
% Normalize Matrix and add steady state Jacobian, note that G is real and symmetric by construction % Normalize Matrix and add steady state Jacobian, note that G is real and symmetric by construction
dSPECTRUM = 2*pi*dSPECTRUM./(2*length(freqs)-1) + dE_y'*dE_y; dSPECTRUM = 2*pi*dSPECTRUM./(2*length(freqs)-1) + dMEAN'*dMEAN;
dSPECTRUM = real(dSPECTRUM); dSPECTRUM = real(dSPECTRUM);
else else
dSPECTRUM = []; dSPECTRUM = [];
@ -482,7 +486,7 @@ if ~no_identification_minimal
-2*Enu*kron(Sigma_e0,Inu)]; -2*Enu*kron(Sigma_e0,Inu)];
dMINIMAL = full([KomunjerNg_DL KomunjerNg_DT KomunjerNg_DU]); dMINIMAL = full([KomunjerNg_DL KomunjerNg_DT KomunjerNg_DU]);
%add Jacobian of steady state (here we also allow for higher-order perturbation, i.e. only the mean provides additional restrictions %add Jacobian of steady state (here we also allow for higher-order perturbation, i.e. only the mean provides additional restrictions
dMINIMAL = [dE_y zeros(obs_nbr,minnx^2+exo_nbr^2); dMINIMAL]; dMINIMAL = [dMEAN zeros(obs_nbr,minnx^2+exo_nbr^2); dMINIMAL];
end end
end end
else else

23
matlab/get_perturbation_params_derivs_numerical_objective.m
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@ -13,22 +13,25 @@ function [out,info] = get_perturbation_params_derivs_numerical_objective(params,
% options: [structure] storing the options % options: [structure] storing the options
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% %
% OUTPUT (dependent on outputflag and order of approximation): % OUTPUT
% - 'perturbation_solution': out = [vec(Sigma_e);vec(ghx);vec(ghu);vec(ghxx);vec(ghxu);vec(ghuu);vec(ghs2);vec(ghxxx);vec(ghxxu);vec(ghxuu);vec(ghuuu);vec(ghxss);vec(ghuss)] % out (dependent on outputflag and order of approximation):
% - 'dynamic_model': out = [Yss; vec(g1); vec(g2); vec(g3)] % - 'perturbation_solution': out = out1 = [vec(Sigma_e);vec(ghx);vec(ghu)]; (order==1)
% - 'Kalman_Transition': out = [Yss; vec(KalmanA); dyn_vech(KalmanB*Sigma_e*KalmanB')]; % out = out2 = [out1;vec(ghxx);vec(ghxu);vec(ghuu);vec(ghs2)]; (order==2)
% all in DR-order % out = out3 = [out1;out2;vec(ghxxx);vec(ghxxu);vec(ghxuu);vec(ghuuu);vec(ghxss);vec(ghuss)]; (order==3)
% - 'dynamic_model': out = [Yss; vec(g1); vec(g2); vec(g3)]
% - 'Kalman_Transition': out = [Yss; vec(KalmanA); dyn_vech(KalmanB*Sigma_e*KalmanB')];
% all in DR-order
% info [integer] output from resol
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function is called by % This function is called by
% * get_solution_params_deriv.m (previously getH.m) % * get_perturbation_params_derivs.m (previously getH.m)
% * get_identification_jacobians.m (previously getJJ.m)
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function calls % This function calls
% * [M.fname,'.dynamic'] % * [M.fname,'.dynamic']
% * resol % * resol
% * dyn_vech % * dyn_vech
% ========================================================================= % =========================================================================
% Copyright (C) 2019 Dynare Team % Copyright (C) 2019-2020 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
@ -48,8 +51,8 @@ function [out,info] = get_perturbation_params_derivs_numerical_objective(params,
%% Update stderr, corr and model parameters and compute perturbation approximation and steady state with updated parameters %% Update stderr, corr and model parameters and compute perturbation approximation and steady state with updated parameters
M = set_all_parameters(params,estim_params,M); M = set_all_parameters(params,estim_params,M);
Sigma_e = M.Sigma_e;
[~,info,M,options,oo] = resol(0,M,options,oo); [~,info,M,options,oo] = resol(0,M,options,oo);
Sigma_e = M.Sigma_e;
if info(1) > 0 if info(1) > 0
% there are errors in the solution algorithm % there are errors in the solution algorithm
@ -60,11 +63,9 @@ else
ghx = oo.dr.ghx; ghu = oo.dr.ghu; ghx = oo.dr.ghx; ghu = oo.dr.ghu;
if options.order > 1 if options.order > 1
ghxx = oo.dr.ghxx; ghxu = oo.dr.ghxu; ghuu = oo.dr.ghuu; ghs2 = oo.dr.ghs2; ghxx = oo.dr.ghxx; ghxu = oo.dr.ghxu; ghuu = oo.dr.ghuu; ghs2 = oo.dr.ghs2;
%ghxs = oo.dr.ghxs; ghus = oo.dr.ghus; %these are zero due to certainty equivalence and Gaussian shocks
end end
if options.order > 2 if options.order > 2
ghxxx = oo.dr.ghxxx; ghxxu = oo.dr.ghxxu; ghxuu = oo.dr.ghxuu; ghxss = oo.dr.ghxss; ghuuu = oo.dr.ghuuu; ghuss = oo.dr.ghuss; ghxxx = oo.dr.ghxxx; ghxxu = oo.dr.ghxxu; ghxuu = oo.dr.ghxuu; ghxss = oo.dr.ghxss; ghuuu = oo.dr.ghuuu; ghuss = oo.dr.ghuss;
%ghxxs = oo.dr.ghxxs; ghxus = oo.dr.ghxus; ghuus = oo.dr.ghuus; ghsss = oo.dr.ghsss; %these are zero due to certainty equivalence and Gaussian shocks
end end
end end
Yss = ys(oo.dr.order_var); %steady state of model variables in DR order Yss = ys(oo.dr.order_var); %steady state of model variables in DR order

36
matlab/identification_analysis.m
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@ -28,7 +28,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% 1: prior exists. Identification is checked for stderr, corr and model parameters as declared in estimated_params block % 1: prior exists. Identification is checked for stderr, corr and model parameters as declared in estimated_params block
% 0: prior does not exist. Identification is checked for all stderr and model parameters, but no corr parameters % 0: prior does not exist. Identification is checked for all stderr and model parameters, but no corr parameters
% * init [integer] % * init [integer]
% flag for initialization of persistent vars. This is needed if one may wants to make more calls to identification in the same dynare mod file % flag for initialization of persistent vars. This is needed if one may want to make more calls to identification in the same mod file
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% OUTPUTS % OUTPUTS
% * ide_moments [structure] % * ide_moments [structure]
@ -36,7 +36,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% * ide_spectrum [structure] % * ide_spectrum [structure]
% identification results using spectrum (Qu and Tkachenko, 2012; Mutschler, 2015) % identification results using spectrum (Qu and Tkachenko, 2012; Mutschler, 2015)
% * ide_minimal [structure] % * ide_minimal [structure]
% identification results using minimal system (Komunjer and Ng, 2011) % identification results using theoretical mean and minimal system (Komunjer and Ng, 2011)
% * ide_hess [structure] % * ide_hess [structure]
% identification results using asymptotic Hessian (Ratto and Iskrev, 2011) % identification results using asymptotic Hessian (Ratto and Iskrev, 2011)
% * ide_reducedform [structure] % * ide_reducedform [structure]
@ -44,7 +44,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% * ide_dynamic [structure] % * ide_dynamic [structure]
% identification results using steady state and dynamic model derivatives (Ratto and Iskrev, 2011) % identification results using steady state and dynamic model derivatives (Ratto and Iskrev, 2011)
% * derivatives_info [structure] % * derivatives_info [structure]
% info about derivatives, used in dsge_likelihood.m % info about first-order perturbation derivatives, used in dsge_likelihood.m
% * info [integer] % * info [integer]
% output from dynare_resolve % output from dynare_resolve
% * options_ident [structure] % * options_ident [structure]
@ -71,7 +71,7 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% * stoch_simul % * stoch_simul
% * vec % * vec
% ========================================================================= % =========================================================================
% Copyright (C) 2008-2019 Dynare Team % Copyright (C) 2008-2020 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
@ -91,16 +91,17 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
global oo_ M_ options_ bayestopt_ estim_params_ global oo_ M_ options_ bayestopt_ estim_params_
persistent ind_dMOMENTS ind_dREDUCEDFORM ind_dDYNAMIC persistent ind_dMOMENTS ind_dREDUCEDFORM ind_dDYNAMIC
% persistence is necessary, because in a MC loop the numerical threshold used may provide vectors of different length, leading to crashes in MC loops % persistent indices are necessary, because in a MC loop the numerical threshold
% used may provide vectors of different length, leading to crashes in MC loops
%initialize output structures %initialize output structures
ide_hess = struct(); %Jacobian of asymptotic/simulated information matrix ide_hess = struct(); %Identification structure based on asymptotic/simulated information matrix
ide_reducedform = struct(); %Jacobian of steady state and reduced form solution ide_reducedform = struct(); %Identification structure based on steady state and reduced form solution
ide_dynamic = struct(); %Jacobian of steady state and dynamic model derivatives ide_dynamic = struct(); %Identification structure based on steady state and dynamic model derivatives
ide_moments = struct(); %Jacobian of first two moments (Iskrev, 2010; Mutschler, 2015) ide_moments = struct(); %Identification structure based on first two moments (Iskrev, 2010; Mutschler, 2015)
ide_spectrum = struct(); %Gram matrix of Jacobian of spectral density plus Gram matrix of Jacobian of steady state (Qu and Tkachenko, 2012; Mutschler, 2015) ide_spectrum = struct(); %Identification structure based on Gram matrix of Jacobian of spectral density plus Gram matrix of Jacobian of steady state (Qu and Tkachenko, 2012; Mutschler, 2015)
ide_minimal = struct(); %Jacobian of minimal system (Komunjer and Ng, 2011) ide_minimal = struct(); %Identification structure based on mean and minimal system (Komunjer and Ng, 2011)
derivatives_info = struct(); %storage for Jacobians used in dsge_likelihood.m for (analytical or simulated) asymptotic Gradient and Hession of likelihood derivatives_info = struct(); %storage for first-order perturbation Jacobians used in dsge_likelihood.m
totparam_nbr = length(params); %number of all parameters to be checked totparam_nbr = length(params); %number of all parameters to be checked
modparam_nbr = length(indpmodel); %number of model parameters to be checked modparam_nbr = length(indpmodel); %number of model parameters to be checked
@ -114,11 +115,6 @@ if ~isempty(estim_params_)
end end
%get options (see dynare_identification.m for description of options) %get options (see dynare_identification.m for description of options)
no_identification_strength = options_ident.no_identification_strength;
no_identification_reducedform = options_ident.no_identification_reducedform;
no_identification_moments = options_ident.no_identification_moments;
no_identification_minimal = options_ident.no_identification_minimal;
no_identification_spectrum = options_ident.no_identification_spectrum;
order = options_ident.order; order = options_ident.order;
nlags = options_ident.ar; nlags = options_ident.ar;
advanced = options_ident.advanced; advanced = options_ident.advanced;
@ -130,11 +126,17 @@ checks_via_subsets = options_ident.checks_via_subsets;
tol_deriv = options_ident.tol_deriv; tol_deriv = options_ident.tol_deriv;
tol_rank = options_ident.tol_rank; tol_rank = options_ident.tol_rank;
tol_sv = options_ident.tol_sv; tol_sv = options_ident.tol_sv;
no_identification_strength = options_ident.no_identification_strength;
no_identification_reducedform = options_ident.no_identification_reducedform;
no_identification_moments = options_ident.no_identification_moments;
no_identification_minimal = options_ident.no_identification_minimal;
no_identification_spectrum = options_ident.no_identification_spectrum;
%Compute linear approximation and fill dr structure %Compute linear approximation and fill dr structure
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_); [oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
if info(1) == 0 %no errors in solution if info(1) == 0 %no errors in solution
% Compute parameter Jacobians for identification analysis
[MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, oo_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs); [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, oo_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs);
if isempty(dMINIMAL) if isempty(dMINIMAL)
% Komunjer and Ng is not computed if (1) minimality conditions are not fullfilled or (2) there are more shocks and measurement errors than observables, so we need to reset options % Komunjer and Ng is not computed if (1) minimality conditions are not fullfilled or (2) there are more shocks and measurement errors than observables, so we need to reset options