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 © 2003-2022 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 <https://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: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>3 && DynareOptions.particle.pruning==1
        error('initial_estimation_checks:: the particle filter with order>3 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.')
    end
end

if DynareOptions.occbin.likelihood.status || DynareOptions.occbin.smoother.status
    if DynareOptions.prefilter
        error('initial_estimation_checks:: Occbin is incompatible with the prefilter option due to the sample mean generally not corresponding to the steady state with an occasionally binding constraint.')
    end
    if ~DynareOptions.occbin.likelihood.inversion_filter && (DynareOptions.kalman_algo==2 || DynareOptions.kalman_algo==4)
        error('initial_estimation_checks:: Occbin is incompatible with the selected univariate Kalman filter.')        
    end
    if DynareOptions.fast_kalman_filter
        error('initial_estimation_checks:: Occbin is incompatible with the fast Kalman filter.')        
    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)<length(DynareOptions.varobs)
            error('initial_estimation_checks:: %s requires as many measurement errors as observed variables',disp_string)
        else
            [~,flag]=chol(Model.H);
            if flag
                error('initial_estimation_checks:: the measurement error matrix must be positive definite')
            end
        end
    end
end

non_zero_ME=length(EstimatedParameters.H_entries_to_check_for_positive_definiteness);

print_init_check_warning=false;
if maximum_number_non_missing_observations>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<implied_inst_nbr
            error('You have specified fewer instruments than there are omitted equations')
        end
    end
end

if ~isreal(DynareDataset.data)
    error('initial_estimation_checks: the data contains complex values.')
end

% Evaluate the likelihood.
ana_deriv = DynareOptions.analytic_derivation;
DynareOptions.analytic_derivation=0;
if ~isequal(DynareOptions.mode_compute,11) || ...
        (isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
    %shut off potentially automatic switch to diffuse filter for the
    %purpose of checking stochastic singularity
    use_univariate_filters_if_singularity_is_detected_old=DynareOptions.use_univariate_filters_if_singularity_is_detected;
    DynareOptions.use_univariate_filters_if_singularity_is_detected=0;
    [fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
    if info(1)==50
        fprintf('\ninitial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.\n')
        fprintf('initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance\n')
        fprintf('initial_estimation_checks:: for a particular combination of parameters and data realizations.\n')
        fprintf('initial_estimation_checks:: If you think the latter is the case, you should try with different initial values for the estimated parameters.\n')
        error('initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.')
    end
    if info(1)==201 || info(1)==202
        message=get_error_message(info,DynareOptions);
        error('initial_estimation_checks:: %s.',message)
    end
    %reset options
    DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
else
    info=0;
    fval = 0;
end
if DynareOptions.debug
    DynareResults.likelihood_at_initial_parameters=fval;
end
DynareOptions.analytic_derivation=ana_deriv;

if DynareOptions.mode_compute==13
    error('Options mode_compute=13 is only compatible with quadratic objective functions')
end


% if DynareOptions.mode_compute==5
%     if ~strcmp(func2str(objective_function),'dsge_likelihood')
%         error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
%     end
% end
if isnan(fval)
    error('The initial value of the likelihood is NaN')
elseif imag(fval)
    error('The initial value of the likelihood is complex')
end

if info(1) > 0
    if DynareOptions.order>1
        [eigenvalues_] = check(Model,DynareOptions, DynareResults);
        if any(abs(1-abs(eigenvalues_))<abs(DynareOptions.qz_criterium-1))
            error('Your model has at least one unit root and you are using a nonlinear filter. Please set nonlinear_filter_initialization=3.')
        end
    else
        disp('Error in computing likelihood for initial parameter values')
        print_info(info, DynareOptions.noprint, DynareOptions)
    end
end

if DynareOptions.prefilter==1
    if (~DynareOptions.loglinear && any(abs(DynareResults.steady_state(BayesInfo.mfys))>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<DynareOptions.mh_tune_jscale.stepsize)
    warning('You specified mh_tune_jscale, but the maximum number of iterations is smaller than the step size. No update will take place.')
end

if ~isempty(DynareOptions.conditional_variance_decomposition) && ~DynareOptions.moments_varendo
    disp('The conditional_variance_decomposition-option will be ignored. You need to set moments_varendo');
end

function evaluate_expression(expression,M_,oo_)
% function evaluate_expression(expression,M_,oo_)
%evaluates expressions relying on M_ and oo_ having their original names
eval(expression);