dynare_estimation_init.m

function [dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)

% function dynare_estimation_init(var_list_, gsa_flag)
% preforms initialization tasks before estimation or
% global sensitivity analysis
%
% INPUTS
%   var_list_:  selected endogenous variables vector
%   dname:      alternative directory name
%   gsa_flag:   flag for GSA operation (optional)
%
% OUTPUTS
%   data:    data after required transformation
%   rawdata:  data as in the data file
%   xparam1:    initial value of estimated parameters as returned by
%               set_prior()
%
% SPECIAL REQUIREMENTS
%   none

% Copyright (C) 2003-2011 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/>.

if isempty(gsa_flag)
    gsa_flag = 0;
else% Decide if a DSGE or DSGE-VAR has to be estimated.
    if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
        options_.dsge_var = 1;
    end
    var_list_ = check_list_of_variables(options_, M_, var_list_);
    options_.varlist = var_list_;
end

% Get the indices of the observed variables in M_.endo_names.
options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
for i = 1:size(M_.endo_names,1)
    tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
    if ~isempty(tmp)
        if length(tmp)>1
            disp(' ')
            error(['Multiple declarations of ' deblank(M_.endo_names(i,:)) ' as an observed variable is not allowed!'])
        end
        options_.lgyidx2varobs(i) = tmp;
    end
end

% Set the order of approximation to one (if needed).
if options_.order > 1 && isempty(options_.nonlinear_filter)
    disp('This version of Dynare cannot estimate non linearized models!')
    disp('Set "order" equal to 1.')
    disp(' ')
    options_.order = 1;
end

% Set options_.lik_init equal to 3 if diffuse filter is used or
% kalman_algo refers to a diffuse filter algorithm.
if (options_.diffuse_filter==1) || (options_.kalman_algo > 2)
    if options_.lik_init == 2
        error(['options diffuse_filter, lik_init and/or kalman_algo have ' ...
               'contradictory settings'])
    else
        options_.lik_init = 3;
    end
end

% If options_.lik_init == 1
%  set by default options_.qz_criterium to 1-1e-6
%  and check options_.qz_criterium < 1-eps if options_.lik_init == 1
% Else set by default options_.qz_criterium to 1+1e-6
if options_.lik_init == 1
    if isempty(options_.qz_criterium)
        options_.qz_criterium = 1-1e-6;
    elseif options_.qz_criterium > 1-eps
        error(['estimation: option qz_criterium is too large for estimating ' ...
               'a stationary model. If your model contains unit roots, use ' ...
               'option diffuse_filter'])
    end
elseif isempty(options_.qz_criterium)
    options_.qz_criterium = 1+1e-6;
end

% If the data are prefiltered then there must not be constants in the
% measurement equation of the DSGE model or in the DSGE-VAR model.
if options_.prefilter == 1
    options_.noconstant = 1;
end

% Set options related to filtered variables.
if ~isequal(options_.filtered_vars,0) && isempty(options_.filter_step_ahead)
    options_.filter_step_ahead = 1;
end
if ~isequal(options_.filtered_vars,0) && isequal(options_.filter_step_ahead,0)
    options_.filter_step_ahead = 1;
end
if ~isequal(options_.filter_step_ahead,0)
    options_.nk = max(options_.filter_step_ahead);
end

% Set the name of the directory where (intermediary) results will be saved.
if isempty(dname)
    M_.dname = M_.fname;
else
    M_.dname = dname;
end

% Set the number of observed variables.
n_varobs = size(options_.varobs,1);

% Set priors over the estimated parameters.
if ~isempty(estim_params_)
    [xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
    if any(bayestopt_.pshape > 0)
        % Plot prior densities.
        if ~options_.nograph && options_.plot_priors
            plot_priors(bayestopt_,M_,estim_params_,options_)
        end
        % Set prior bounds
        bounds = prior_bounds(bayestopt_,options_);
        bounds(:,1)=max(bounds(:,1),lb);
        bounds(:,2)=min(bounds(:,2),ub);
    else
        % No priors are declared so Dynare will estimate the model by
        % maximum likelihood with inequality constraints for the parameters.
        options_.mh_replic = 0;% No metropolis.
        bounds(:,1) = lb;
        bounds(:,2) = ub;
    end
    % Test if initial values of the estimated parameters are all between
    % the prior lower and upper bounds.
    if any(xparam1 < bounds(:,1)) || any(xparam1 > bounds(:,2))
        find(xparam1 < bounds(:,1))
        find(xparam1 > bounds(:,2))
        error('Initial parameter values are outside parameter bounds')
    end
    lb = bounds(:,1);
    ub = bounds(:,2);
    bayestopt_.lb = lb;
    bayestopt_.ub = ub;
else% If estim_params_ is empty...
    xparam1 = [];
    bayestopt_.lb = [];
    bayestopt_.ub = [];
    bayestopt_.jscale = [];
    bayestopt_.pshape = [];
    bayestopt_.p1 = [];
    bayestopt_.p2 = [];
    bayestopt_.p3 = [];
    bayestopt_.p4 = [];
    bayestopt_.p5 = [];
    bayestopt_.p6 = [];
    bayestopt_.p7 = [];
    estim_params_.nvx = 0;
    estim_params_.nvn = 0;
    estim_params_.ncx = 0;
    estim_params_.ncn = 0;
    estim_params_.np = 0;
end

% Is there a linear trend in the measurement equation?
if ~isfield(options_,'trend_coeffs') % No!
    bayestopt_.with_trend = 0;
else% Yes!
    bayestopt_.with_trend = 1;
    bayestopt_.trend_coeff = {};
    trend_coeffs = options_.trend_coeffs;
    nt = length(trend_coeffs);
    for i=1:n_varobs
        if i > length(trend_coeffs)
            bayestopt_.trend_coeff{i} = '0';
        else
            bayestopt_.trend_coeff{i} = trend_coeffs{i};
        end
    end
end

% Set the "size" of penalty.
bayestopt_.penalty = 1e8;

% Get informations about the variables of the model.
dr = set_state_space(oo_.dr,M_);
oo_.dr = dr;
nstatic = dr.nstatic;          % Number of static variables.
npred = dr.npred;              % Number of predetermined variables.
nspred = dr.nspred;            % Number of predetermined variables in the state equation.

% Test if observed variables are declared.
if isempty(options_.varobs)
    error('VAROBS is missing')
end

% Setting resticted state space (observed + predetermined variables)
var_obs_index = [];
k1 = [];
for i=1:n_varobs
    var_obs_index = [var_obs_index; strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
    k1 = [k1; strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
end

% Define union of observed and state variables
k2 = union(var_obs_index,[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = k2;
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2  = var_obs_index;
bayestopt_.mfys = k1;

[junk,ic] = intersect(k2,nstatic+(1:npred)');
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];

k3 = [];
k3p = [];
if options_.selected_variables_only
    for i=1:size(var_list_,1)
        k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), ...
                           'exact')];
        k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, ...
                           'exact')];
    end
else
    k3 = (1:M_.endo_nbr)';
    k3p = (1:M_.endo_nbr)';
end

% Define union of observed and state variables
if options_.block == 1
    [k2, i_posA, i_posB] = union(k1', M_.state_var', 'rows');
    % Set restrict_state to postion of observed + state variables in expanded state vector.
    oo_.dr.restrict_var_list  = [k1(i_posA) M_.state_var(sort(i_posB))];
    % set mf0 to positions of state variables in restricted state vector for likelihood computation.
    [junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
    % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
    [junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
    % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
    bayestopt_.mf2  = var_obs_index;
    bayestopt_.mfys = k1;
    oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];

    [k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
    bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
    [junk,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
    [junk,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
    bayestopt_.smoother_restrict_columns = ic;
    [junk,bayestopt_.smoother_mf] = ismember(k1, ...
                                             bayestopt_.smoother_var_list);
else
    k2 = union(var_obs_index,[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
    % Set restrict_state to postion of observed + state variables in expanded state vector.
    oo_.dr.restrict_var_list = k2;
    % set mf0 to positions of state variables in restricted state vector for likelihood computation.
    [junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
    % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
    [junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
    % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
    bayestopt_.mf2  = var_obs_index;
    bayestopt_.mfys = k1;
    [junk,ic] = intersect(k2,nstatic+(1:npred)');
    oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];

    bayestopt_.smoother_var_list = union(k2,k3);
    [junk,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
    [junk,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
    bayestopt_.smoother_restrict_columns = ic;
    [junk,bayestopt_.smoother_mf] = ismember(var_obs_index, ...
                                             bayestopt_.smoother_var_list);
end;

% Test if the data file is declared.
if isempty(options_.datafile)
    if gsa_flag
        dataset_ = [];
%         rawdata = [];
%         data_info = [];
        return
    else
        error('datafile option is missing')
    end
end

% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
k = find(isnan(bayestopt_.jscale));
bayestopt_.jscale(k) = options_.mh_jscale;

% Load and transform data.
transformation = [];
if options_.loglinear && ~options_.logdata
    transformation = @log;
end
xls.sheet = options_.xls_sheet;
xls.range = options_.xls_range;

if ~isfield(options_,'nobs')
    options_.nobs = [];
end

dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);

options_.nobs = dataset_.info.ntobs;

% setting noconstant option
if options_.diffuse_filter
    steadystate_check_flag = 0;
else
    steadystate_check_flag = 1;
end

oo_.steady_state = evaluate_steady_state(oo_.steady_state,M_,options_,oo_,steadystate_check_flag);
if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
    options_.noconstant = 1;
else
    options_.noconstant = 0;
end