function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
% function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
% Checks data (complex values, ML evaluation, initial values, BK conditions,..)
%
% INPUTS
% objective_function [function handle] of the objective function
% xparam1: [vector] of parameters to be estimated
% DynareDataset: [dseries] object storing the dataset
% DataSetInfo: [structure] storing informations about the sample.
% Model: [structure] decribing the model
% EstimatedParameters [structure] characterizing parameters to be estimated
% DynareOptions [structure] describing the options
% BayesInfo [structure] describing the priors
% BoundsInfo [structure] containing prior bounds
% DynareResults [structure] storing the results
%
% OUTPUTS
% DynareResults structure of temporary results
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2018 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see .
%get maximum number of simultaneously observed variables for stochastic
%singularity check
maximum_number_non_missing_observations=max(sum(~isnan(DynareDataset.data(2:end,:)),2));
init_number_non_missing_observations=sum(~isnan(DynareDataset.data(1,:)),2);
if DynareOptions.heteroskedastic_filter
if DynareOptions.order>1
error('initial_estimation_checks:: heteroskedastic shocks are only supported with the Kalman filter/smoother')
end
observations_by_period=sum(~isnan(DynareDataset.data),2);
base_shocks = find(diag(Model.Sigma_e));
zero_shocks = ~ismember(1:Model.exo_nbr,base_shocks);
non_zero_shocks_by_period=repmat(length(base_shocks),size(observations_by_period));
% check periods for which base shocks are scaled to zero
non_zero_shocks_by_period = non_zero_shocks_by_period-sum(Model.heteroskedastic_shocks.Qscale(base_shocks,1:DynareOptions.nobs)==0,1)';
% check periods for which base shocks are set to zero
non_zero_shocks_by_period = non_zero_shocks_by_period-sum(Model.heteroskedastic_shocks.Qvalue(base_shocks,1:DynareOptions.nobs)==0,1)';
% check periods for which other shocks are set to a positive number
non_zero_shocks_by_period = non_zero_shocks_by_period+sum(Model.heteroskedastic_shocks.Qvalue(zero_shocks,1:DynareOptions.nobs)>0,1)';
end
if DynareOptions.order>1
if any(any(isnan(DynareDataset.data)))
error('initial_estimation_checks:: particle filtering does not support missing observations')
end
if DynareOptions.prefilter==1
error('initial_estimation_checks:: particle filtering does not support the prefilter option')
end
if BayesInfo.with_trend
error('initial_estimation_checks:: particle filtering does not support trends')
end
if DynareOptions.order>2 && DynareOptions.particle.pruning==1
error('initial_estimation_checks:: the particle filter with order>2 does not support pruning')
end
if DynareOptions.particle.pruning~=DynareOptions.pruning
warning('initial_estimation_checks:: the pruning settings differ between the particle filter and the one used for IRFs/simulations. Make sure this is intended.\n')
end
end
if (DynareOptions.occbin.likelihood.status && DynareOptions.occbin.likelihood.inversion_filter) || (DynareOptions.occbin.smoother.status && DynareOptions.occbin.smoother.inversion_filter)
err_index= find(diag(Model.Sigma_e)~=0);
if length(err_index)~=length(DynareOptions.varobs)
fprintf('initial_estimation_checks:: The IVF requires exactly as many shocks as observables.')
end
var_index=find(any(isnan(DynareDataset.data)));
if ~isempty(var_index)
fprintf('initial_estimation_checks:: The IVF requires exactly as many shocks as observables.\n')
fprintf('initial_estimation_checks:: The data series %s contains NaN, I am therefore dropping shock %s for these time points.\n',...
DynareOptions.varobs{var_index},Model.exo_names{DynareOptions.occbin.likelihood.IVF_shock_observable_mapping(var_index)})
end
end
if DynareOptions.order>1 || (DynareOptions.order==1 && ~ischar(DynareOptions.mode_compute) && DynareOptions.mode_compute==11)
if DynareOptions.order==1 && DynareOptions.mode_compute==11
disp_string='mode_compute=11';
else
disp_string='particle filtering';
end
if Model.H==0
error('initial_estimation_checks:: %s requires measurement error on the observables',disp_string)
else
if sum(diag(Model.H)>0)Model.exo_nbr+non_zero_ME
error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
end
if init_number_non_missing_observations>Model.exo_nbr+non_zero_ME
if DynareOptions.no_init_estimation_check_first_obs
print_init_check_warning=true;
else
error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables in first period!'])
end
end
if DynareOptions.heteroskedastic_filter
if any(observations_by_period>(non_zero_shocks_by_period+non_zero_ME))
error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance: Check heteroskedastic block and shocks calibration!'])
end
else
if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+non_zero_ME
error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
end
end
if init_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+non_zero_ME
if DynareOptions.no_init_estimation_check_first_obs
print_init_check_warning=true;
else
error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance in first period!'])
end
end
if print_init_check_warning
fprintf('ESTIMATION_CHECKS: You decided to ignore test of stochastic singularity in first_obs.\n');
fprintf('ESTIMATION_CHECKS: If this was not done on purpose (typically when observing a stock variable [capital] in first period, on top of its flow [investment]),\n');
fprintf('ESTIMATION_CHECKS: it may lead to a crash or provide undesired/wrong results later on!\n');
end
if (any(BayesInfo.pshape >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_nblck<1
error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
end
old_steady_params=Model.params; %save initial parameters for check if steady state changes param values
% % check if steady state solves static model (except if diffuse_filter == 1)
[DynareResults.steady_state, new_steady_params] = evaluate_steady_state(DynareResults.steady_state,Model,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.param_vals)
%check whether steady state file changes estimated parameters
Model_par_varied=Model; %store Model structure
Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters
[~, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
| (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
if ~isempty(changed_par_indices)
fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
disp(char(Model.param_names(EstimatedParameters.param_vals(changed_par_indices,1))))
fprintf('This will override the parameter values drawn from the proposal density and may lead to wrong results.\n')
fprintf('Check whether this is really intended.\n')
warning('The steady state file internally changes the values of the estimated parameters.')
end
end
if any(BayesInfo.pshape) % if Bayesian estimation
nvx=EstimatedParameters.nvx;
if nvx && any(BayesInfo.p3(1:nvx)<0)
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.')
end
offset=nvx;
nvn=EstimatedParameters.nvn;
if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
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.')
end
offset = nvx+nvn;
ncx=EstimatedParameters.ncx;
if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
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')
end
offset = nvx+nvn+ncx;
ncn=EstimatedParameters.ncn;
if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
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')
end
end
% display warning if some parameters are still NaN
test_for_deep_parameters_calibration(Model);
[lnprior,~,~,info]= priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
if any(info)
fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',BayesInfo.name{info,1})
error('The initial value of the prior is -Inf')
end
if isfield(Model,'filter_initial_state') && ~isempty(Model.filter_initial_state)
state_indices=DynareResults.dr.order_var(DynareResults.dr.restrict_var_list(BayesInfo.mf0));
for ii=1:size(state_indices,1)
if ~isempty(Model.filter_initial_state{state_indices(ii),1})
try
evaluate_expression(Model.filter_initial_state{state_indices(ii),2},Model,DynareResults)
catch
fprintf('Unable to evaluate the expression\n %s \nfor the filter_initial_state of variable %s\n',Model.filter_initial_state{state_indices(ii),2},Model.endo_names(state_indices(ii),:))
end
end
end
end
if DynareOptions.ramsey_policy
%test whether specification matches
inst_nbr = size(DynareOptions.instruments,1);
if inst_nbr~=0
orig_endo_aux_nbr = Model.orig_endo_nbr + min(find([Model.aux_vars.type] == 6)) - 1;
implied_inst_nbr = orig_endo_aux_nbr - Model.orig_eq_nbr;
if inst_nbr>implied_inst_nbr
error('You have specified more instruments than there are omitted equations')
elseif inst_nbr 0
if DynareOptions.order>1
[eigenvalues_] = check(Model,DynareOptions, DynareResults);
if any(abs(1-abs(eigenvalues_))1e-9)) || (DynareOptions.loglinear && any(abs(log(DynareResults.steady_state(BayesInfo.mfys)))>1e-9))
disp(['You are trying to estimate a model with a non zero steady state for the observed endogenous'])
disp(['variables using demeaned data!'])
error('You should change something in your mod file...')
end
end
if ~isequal(DynareOptions.mode_compute,11)
disp(['Initial value of the log posterior (or likelihood): ' num2str(-fval)]);
end
if DynareOptions.mh_tune_jscale.status && (DynareOptions.mh_tune_jscale.maxiter