190 lines
9.8 KiB
Matlab
190 lines
9.8 KiB
Matlab
function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
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% function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
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% Checks data (complex values, ML evaluation, initial values, BK conditions,..)
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%
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% INPUTS
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% objective_function [function handle] of the objective function
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% xparam1: [vector] of parameters to be estimated
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% DynareDataset: [dseries] object storing the dataset
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% DataSetInfo: [structure] storing informations about the sample.
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% Model: [structure] decribing the model
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% EstimatedParameters [structure] characterizing parameters to be estimated
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% DynareOptions [structure] describing the options
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% BayesInfo [structure] describing the priors
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% BoundsInfo [structure] containing prior bounds
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% DynareResults [structure] storing the results
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%
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% OUTPUTS
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% DynareResults structure of temporary results
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2018 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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%get maximum number of simultaneously observed variables for stochastic
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%singularity check
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maximum_number_non_missing_observations=max(sum(~isnan(DynareDataset.data),2));
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if DynareOptions.order>1 && any(any(isnan(DynareDataset.data)))
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error('initial_estimation_checks:: particle filtering does not support missing observations')
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end
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if maximum_number_non_missing_observations>Model.exo_nbr+EstimatedParameters.nvn
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error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
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end
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if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
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error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
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end
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if (any(BayesInfo.pshape >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_nblck<1
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error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
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end
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old_steady_params=Model.params; %save initial parameters for check if steady state changes param values
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% % check if steady state solves static model (except if diffuse_filter == 1)
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[DynareResults.steady_state, new_steady_params] = evaluate_steady_state(DynareResults.steady_state,Model,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
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if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.param_vals)
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%check whether steady state file changes estimated parameters
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Model_par_varied=Model; %store Model structure
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Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters
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[junk, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
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changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
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| (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
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if ~isempty(changed_par_indices)
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fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
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disp(char(Model.param_names(EstimatedParameters.param_vals(changed_par_indices,1))))
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fprintf('This will override the parameter values drawn from the proposal density and may lead to wrong results.\n')
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fprintf('Check whether this is really intended.\n')
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warning('The steady state file internally changes the values of the estimated parameters.')
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end
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end
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if any(BayesInfo.pshape) % if Bayesian estimation
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nvx=EstimatedParameters.nvx;
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if nvx && any(BayesInfo.p3(1:nvx)<0)
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warning('Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
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end
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offset=nvx;
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nvn=EstimatedParameters.nvn;
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if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
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warning('Your prior allows for negative standard deviations for measurement error. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
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end
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offset = nvx+nvn;
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ncx=EstimatedParameters.ncx;
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if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
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warning('Your prior allows for correlations between structural shocks larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
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end
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offset = nvx+nvn+ncx;
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ncn=EstimatedParameters.ncn;
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if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
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warning('Your prior allows for correlations between measurement errors larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
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end
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end
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% display warning if some parameters are still NaN
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test_for_deep_parameters_calibration(Model);
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[lnprior, junk1,junk2,info]= priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
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if info
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fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',BayesInfo.name{info,1})
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error('The initial value of the prior is -Inf')
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end
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if DynareOptions.ramsey_policy
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%test whether specification matches
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inst_nbr = size(DynareOptions.instruments,1);
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if inst_nbr~=0
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orig_endo_aux_nbr = Model.orig_endo_nbr + min(find([Model.aux_vars.type] == 6)) - 1;
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implied_inst_nbr = orig_endo_aux_nbr - Model.orig_eq_nbr;
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if inst_nbr>implied_inst_nbr
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error('You have specified more instruments than there are omitted equations')
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elseif inst_nbr<implied_inst_nbr
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error('You have specified fewer instruments than there are omitted equations')
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end
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end
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end
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% Evaluate the likelihood.
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ana_deriv = DynareOptions.analytic_derivation;
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DynareOptions.analytic_derivation=0;
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if ~isequal(DynareOptions.mode_compute,11) || ...
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(isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
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%shut off potentially automatic switch to diffuse filter for the
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%purpose of checking stochastic singularity
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use_univariate_filters_if_singularity_is_detected_old=DynareOptions.use_univariate_filters_if_singularity_is_detected;
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DynareOptions.use_univariate_filters_if_singularity_is_detected=0;
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[fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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if info(1)==50
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fprintf('\ninitial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.\n')
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fprintf('initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance\n')
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fprintf('initial_estimation_checks:: for a particular combination of parameters and data realizations.\n')
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fprintf('initial_estimation_checks:: If you think the latter is the case, you should try with different initial values for the estimated parameters.\n')
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error('initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.')
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end
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%reset options
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DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
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else
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info=0;
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fval = 0;
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end
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if DynareOptions.debug
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DynareResults.likelihood_at_initial_parameters=fval;
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end
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DynareOptions.analytic_derivation=ana_deriv;
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% if DynareOptions.mode_compute==5
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% if ~strcmp(func2str(objective_function),'dsge_likelihood')
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% error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
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% end
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% end
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if isnan(fval)
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error('The initial value of the likelihood is NaN')
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elseif imag(fval)
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error('The initial value of the likelihood is complex')
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end
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if info(1) > 0
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if DynareOptions.order>1
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[eigenvalues_] = check(Model,DynareOptions, DynareResults);
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if any(abs(1-abs(eigenvalues_))<abs(DynareOptions.qz_criterium-1))
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error('Your model has at least one unit root and you are using a nonlinear filter. Please set nonlinear_filter_initialization=3.')
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end
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else
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disp('Error in computing likelihood for initial parameter values')
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print_info(info, DynareOptions.noprint, DynareOptions)
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end
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end
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if DynareOptions.prefilter==1
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if (~DynareOptions.loglinear && any(abs(DynareResults.steady_state(BayesInfo.mfys))>1e-9)) || (DynareOptions.loglinear && any(abs(log(DynareResults.steady_state(BayesInfo.mfys)))>1e-9))
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disp(['You are trying to estimate a model with a non zero steady state for the observed endogenous'])
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disp(['variables using demeaned data!'])
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error('You should change something in your mod file...')
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end
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end
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if ~isequal(DynareOptions.mode_compute,11)
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disp(['Initial value of the log posterior (or likelihood): ' num2str(-fval)]);
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end
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