initial_estimation_checks.m

function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
% function initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
% 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
%   DynareResults       [structure] storing the results
%
% OUTPUTS
%    DynareResults     structure of temporary results
%
% SPECIAL REQUIREMENTS
%    none

% Copyright (C) 2003-2015 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 <http://www.gnu.org/licenses/>.

%get maximum number of simultaneously observed variables for stochastic
%singularity check
maximum_number_non_missing_observations=max(sum(~isnan(DynareDataset.data),2));

if maximum_number_non_missing_observations>Model.exo_nbr+EstimatedParameters.nvn
    error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
end

if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
    error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
end

if ~(strcmpi(DynareOptions.proposal_distribution, 'rand_multivariate_student') || ...
     strcmpi(DynareOptions.proposal_distribution, 'rand_multivariate_normal'))
    error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
        'rand_multivariate_student or rand_multivariate_normal as options']);
end

if DynareOptions.student_degrees_of_freedom <= 0
    error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
end

if DynareOptions.TaRB.use_TaRB && (DynareOptions.TaRB.new_block_probability<0 || DynareOptions.TaRB.new_block_probability>1)
    error(['initial_estimation_checks:: The tarb_new_block_probability must be between 0 and 1!'])
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)