257 lines
8.1 KiB
Matlab
257 lines
8.1 KiB
Matlab
function dynare_MC(var_list_)
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global M_ options_ oo_ estim_params_
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global bayestopt_
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options_.varlist = var_list_;
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options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
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for i = 1:size(M_.endo_names,1)
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tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
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if ~isempty(tmp)
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options_.lgyidx2varobs(i,1) = tmp;
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end
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end
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options_ = set_default_option(options_,'first_obs',1);
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options_ = set_default_option(options_,'prefilter',0);
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options_ = set_default_option(options_,'presample',0);
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options_ = set_default_option(options_,'lik_algo',1);
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options_ = set_default_option(options_,'lik_init',1);
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options_ = set_default_option(options_,'nograph',0);
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options_ = set_default_option(options_,'mh_conf_sig',0.90);
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options_ = set_default_option(options_,'mh_replic',20000);
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options_ = set_default_option(options_,'mh_drop',0.5);
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options_ = set_default_option(options_,'mh_jscale',0.2);
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options_ = set_default_option(options_,'mh_init_scale',2*options_.mh_jscale);
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options_ = set_default_option(options_,'mode_file','');
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options_ = set_default_option(options_,'mode_compute',4);
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options_ = set_default_option(options_,'mode_check',0);
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options_ = set_default_option(options_,'prior_trunc',1e-10);
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options_ = set_default_option(options_,'mh_mode',1);
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options_ = set_default_option(options_,'mh_nblck',2);
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options_ = set_default_option(options_,'load_mh_file',0);
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options_ = set_default_option(options_,'nodiagnostic',0);
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options_ = set_default_option(options_,'loglinear',0);
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options_ = set_default_option(options_,'unit_root_vars',[]);
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options_ = set_default_option(options_,'XTick',[]);
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options_ = set_default_option(options_,'XTickLabel',[]);
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options_ = set_default_option(options_,'bayesian_irf',0);
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options_ = set_default_option(options_,'bayesian_th_moments',0);
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options_ = set_default_option(options_,'TeX',0);
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options_ = set_default_option(options_,'irf',0);
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options_ = set_default_option(options_,'relative_irf',0);
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options_ = set_default_option(options_,'order',1);
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options_ = set_default_option(options_,'ar',5);
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options_ = set_default_option(options_,'dr_algo',0);
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options_ = set_default_option(options_,'linear',0);
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options_ = set_default_option(options_,'drop',0);
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options_ = set_default_option(options_,'replic',1);
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options_ = set_default_option(options_,'hp_filter',0);
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options_ = set_default_option(options_,'forecast',0);
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options_ = set_default_option(options_,'smoother',0);
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options_ = set_default_option(options_,'moments_varendo',0);
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options_ = set_default_option(options_,'filtered_vars',0);
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options_ = set_default_option(options_,'kalman_algo',1);
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options_ = set_default_option(options_,'kalman_tol',10^(-12));
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options_ = set_default_option(options_,'posterior_mode_estimation',1);
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options_ = set_default_option(options_,'MaxNumberOfBytes',1e6);
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options_ = set_default_option(options_,'xls_sheet','');
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options_ = set_default_option(options_,'xls_range','');
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%% Add something to the parser ++>
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M_.dname = M_.fname; % The user should be able to choose another name
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% for the directory...
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pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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n_varobs = size(options_.varobs,1);
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[xparam1,estim_params_,bayestopt_,lb,ub] = set_prior(estim_params_);
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options_.mh_replic = 0;
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bounds = prior_bounds(bayestopt_);
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bounds(:,1)=max(bounds(:,1),lb);
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bounds(:,2)=min(bounds(:,2),ub);
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if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
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find(xparam1 < bounds(:,1))
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find(xparam1 > bounds(:,2))
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error('Initial parameter values are outside parameter bounds')
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end
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lb = bounds(:,1);
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ub = bounds(:,2);
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bayestopt_.lb = lb;
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bayestopt_.ub = ub;
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if ~isfield(options_,'trend_coeffs')
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bayestopt_.with_trend = 0;
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else
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bayestopt_.with_trend = 1;
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bayestopt_.trend_coeff = {};
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trend_coeffs = options_.trend_coeffs;
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nt = length(trend_coeffs);
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for i=1:n_varobs
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if i > length(trend_coeffs)
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bayestopt_.trend_coeff{i} = '0';
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else
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bayestopt_.trend_coeff{i} = trend_coeffs{i};
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end
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end
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end
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bayestopt_.penalty = 1e8; % penalty
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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nx = nvx+nvn+ncx+ncn+np;
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%% Static solver
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if exist([M_.fname '_steadystate'])
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bayestopt_.static_solve = [M_.fname '_steadystate'];
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else
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bayestopt_.static_solve = 'dynare_solve';
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end
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dr = set_state_space([]);
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%% Initialization with unit-root variables
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if ~isempty(options_.unit_root_vars)
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n_ur = size(options_.unit_root_vars,1);
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i_ur = zeros(n_ur,1);
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for i=1:n_ur
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i1 = strmatch(deblank(options_.unit_root_vars(i,:)),M_.endo_names(dr.order_var,:),'exact');
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if isempty(i1)
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error('Undeclared variable in unit_root_vars statement')
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end
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i_ur(i) = i1;
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end
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if M_.maximum_lag > 1
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l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
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n1 = nnz(l1);
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bayestopt_.Pinf = zeros(n1,n1);
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l2 = find(l1');
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l3 = zeros(M_.endo_nbr,M_.maximum_lag);
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l3(i_ur,:) = l1(:,i_ur)';
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l3 = l3(:);
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i_ur1 = find(l3(l2));
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i_stable = ones(M_.endo_nbr,1);
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i_stable(i_ur) = zeros(n_ur,1);
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i_stable = find(i_stable);
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bayestopt_.Pinf(i_ur1,i_ur1) = diag(ones(1,length(i_ur1)));
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bayestopt_.i_var_stable = i_stable;
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l3 = zeros(M_.endo_nbr,M_.maximum_lag);
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l3(i_stable,:) = l1(:,i_stable)';
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l3 = l3(:);
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bayestopt_.i_T_var_stable = find(l3(l2));
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else
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n1 = M_.endo_nbr;
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bayestopt_.Pinf = zeros(n1,n1);
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bayestopt_.Pinf(i_ur,i_ur) = diag(ones(1,length(i_ur)));
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l1 = ones(M_.endo_nbr,1);
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l1(i_ur,:) = zeros(length(i_ur),1);
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bayestopt_.i_T_var_stable = find(l1);
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end
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options_.lik_init = 3;
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end % if ~isempty(options_.unit_root_vars)
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if isempty(options_.datafile)
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error('ESTIMATION: datafile option is missing')
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end
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if isempty(options_.varobs)
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error('ESTIMATION: VAROBS is missing')
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end
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%% If jscale isn't specified for an estimated parameter, use
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%% global option options_.jscale, set to 0.2, by default
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k = find(isnan(bayestopt_.jscale));
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bayestopt_.jscale(k) = options_.mh_jscale;
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%% Read and demean data
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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k = [];
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k1 = [];
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for i=1:n_varobs
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k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:), ...
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'exact')];
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k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
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end
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bayestopt_.mf = k;
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bayestopt_.mfys = k1;
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options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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gend = options_.nobs;
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rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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if options_.loglinear == 1
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rawdata = log(rawdata);
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end
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if options_.prefilter == 1
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bayestopt_.mean_varobs = mean(rawdata,1);
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data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
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else
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data = transpose(rawdata);
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end
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if ~isreal(rawdata)
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error(['There are complex values in the data. Probably a wrong' ...
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' transformation'])
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end
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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npar = nvx+nvn+ncx+ncn+np;
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offset = npar-np;
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fname_=M_.fname;
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options_ = set_default_option(options_,'opt_gsa',1);
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options_gsa_ = options_.opt_gsa;
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if options_gsa_.pprior,
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load([fname_,'_prior'])
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namfile=[fname_,'_prior'];
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else
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load([fname_,'_mc'])
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namfile=[fname_,'_mc'];
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end
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load(options_.mode_file)
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%%
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%%
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%%
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x=[lpmat0(istable,:) lpmat(istable,:)];
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clear lpmat lpmat0 istable iunstable egg yys T
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B = size(x,1);
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if M_.maximum_lag > 1
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l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
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n1 = nnz(l1);
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else
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n1 = M_.endo_nbr;
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end
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stock_smooth = zeros(gend,n1,B);
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stock_filter = zeros(gend+1,n1,B);
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stock_ys = zeros(M_.endo_nbr,B);
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%%
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h = waitbar(0,'MC smoother ...');
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for b=1:B
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deep = x(b,:);
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%deep(1:offset) = xparam1(1:offset);
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logpo2(b) = DsgeLikelihood(deep',gend,data);
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[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(deep,gend,data);
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stock_smooth(:,:,b)=atT';
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stock_filter(:,:,b)=filtered_state_vector';
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stock_ys(:,b)=ys;
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waitbar(b/B,h,['MC smoother ...',num2str(b),'/',num2str(B)]);
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end
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close(h)
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save(namfile,'x','logpo2','stock_smooth','stock_filter','stock_ys','-append')
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