SUR with test
Houtan Bastani
parent
0301029a17
commit
3cd4e732d3
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@ -0,0 +1,228 @@
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function sur(ds)
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% function sur(ds)
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% Seemingly Unrelated Regressions
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%
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% INPUTS
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% ds [dseries] data to use in estimation
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% dynare must be run with the option: json=parse
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% Copyright (C) 2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ oo_ options_
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%% Check input argument
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assert(~isempty(ds) && isdseries(ds), 'The first argument must be a dseries');
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%% Read JSON
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jsonfile = [M_.fname '_original.json'];
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if exist(jsonfile, 'file') ~= 2
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error('Could not find %s! Please use the json=parse option (See the Dynare invocation section in the reference manual).', jsonfile);
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end
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jsonmodel = loadjson(jsonfile);
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jsonmodel = jsonmodel.model;
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[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
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%% Find parameters and variable names in equations and setup estimation matrices
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M_exo_names_trim = cellstr(M_.exo_names);
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M_endo_exo_names_trim = [cellstr(M_.endo_names); M_exo_names_trim];
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M_param_names_trim = cellstr(M_.param_names);
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regex = strjoin(M_endo_exo_names_trim(:,1), '|');
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mathops = '[\+\*\^\-\/]';
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params = cell(length(rhs),1);
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vars = cell(length(rhs),1);
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Y = [];
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X = [];
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startidxs = zeros(length(lhs), 1);
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startdates = cell(length(lhs), 1);
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enddates = cell(length(lhs), 1);
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residnames = cell(length(lhs), 1);
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pidxs = zeros(M_.param_nbr, 1);
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pidx = 0;
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vnamesall = {};
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for i = 1:length(lhs)
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rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','ln(','log10(','exp(','(',')','diff('});
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rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
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vnames = setdiff(rhs_, M_param_names_trim);
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if ~isempty(regexp(rhs{i}, ...
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['(' strjoin(vnames, '\\(\\d+\\)|') '\\(\\d+\\))'], ...
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'once'))
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error(['sur1: you cannot have leads in equation on line ' ...
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lineno{i} ': ' lhs{i} ' = ' rhs{i}]);
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end
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% Find parameters and associated variables
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pnames = intersect(rhs_, M_param_names_trim);
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vnames = cell(1, length(pnames));
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xjdata = dseries;
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for j = 1:length(pnames)
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pidx = pidx + 1;
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pidxs(pidx, 1) = find(strcmp(pnames{j}, M_param_names_trim));
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createdvar = false;
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pregex = [...
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mathops pnames{j} mathops ...
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'|^' pnames{j} mathops ...
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'|' mathops pnames{j} '$' ...
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];
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[startidx, endidx] = regexp(rhs{i}, pregex, 'start', 'end');
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assert(length(startidx) == 1);
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if rhs{i}(startidx) == '*'
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vnames{j} = getStrMoveLeft(rhs{i}(1:startidx-1));
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elseif rhs{i}(endidx) == '*'
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vnames{j} = getStrMoveRight(rhs{i}(endidx+1:end));
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elseif rhs{i}(startidx) == '+' ...
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|| rhs{i}(startidx) == '-' ...
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|| rhs{i}(endidx) == '+' ...
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|| rhs{i}(endidx) == '-'
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% intercept
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createdvar = true;
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if any(strcmp(M_endo_exo_names_trim, 'intercept'))
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[~, vnames{j}] = fileparts(tempname);
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vnames{j} = ['intercept_' vnames{j}];
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assert(~any(strcmp(M_endo_exo_names_trim, vnames{j})));
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else
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vnames{j} = 'intercept';
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end
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else
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error('sur1: Shouldn''t arrive here');
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end
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if createdvar
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xjdatatmp = dseries(ones(ds.nobs, 1), ds.firstdate, vnames{j});
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else
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xjdatatmp = eval(regexprep(vnames{j}, regex, 'ds.$&'));
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xjdatatmp.rename_(vnames{j});
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end
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xjdatatmp.rename_(num2str(j));
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xjdata = [xjdata xjdatatmp];
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end
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residuals = intersect(rhs_, cellstr(M_.exo_names));
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for j = 1:length(residuals)
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if any(strcmp(residuals{j}, vnames))
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residuals{j} = [];
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end
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end
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idx = ~cellfun(@isempty, residuals);
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assert(sum(idx) == 1, ['More than one residual in equation ' num2str(i)]);
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residnames{i} = residuals{idx};
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params{i} = pnames;
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vars{i} = vnames;
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ydata = eval(regexprep(lhs{i}, regex, 'ds.$&'));
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fp = max(ydata.firstobservedperiod, xjdata.firstobservedperiod);
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lp = min(ydata.lastobservedperiod, xjdata.lastobservedperiod);
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startidxs(i) = length(Y) + 1;
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startdates{i} = fp;
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enddates{i} = lp;
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Y(startidxs(i):startidxs(i)+lp-fp, 1) = ydata(fp:lp).data;
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X(startidxs(i):startidxs(i)+lp-fp, end+1:end+size(xjdata(fp:lp).data,2)) = xjdata(fp:lp).data;
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end
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assert(size(X, 2) == M_.param_nbr, 'Not all parameters were used in model');
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%% Force equations to have the same sample range
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maxfp = max([startdates{:}]);
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minlp = min([enddates{:}]);
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nobs = minlp - maxfp;
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newY = zeros(nobs*length(lhs), 1);
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newX = zeros(nobs*length(lhs), columns(X));
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lastidx = 1;
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for i = 1:length(lhs)
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if i == length(lhs)
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yds = dseries(Y(startidxs(i):end), startdates{i});
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xds = dseries(X(startidxs(i):end, :), startdates{i});
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else
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yds = dseries(Y(startidxs(i):startidxs(i+1)-1), startdates{i});
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xds = dseries(X(startidxs(i):startidxs(i+1)-1, :), startdates{i});
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end
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newY(lastidx:lastidx + nobs, 1) = yds(maxfp:minlp).data;
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newX(lastidx:lastidx + nobs, :) = xds(maxfp:minlp, :).data;
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if i ~= length(lhs)
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lastidx = lastidx + nobs + 1;
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end
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end
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Y = newY;
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X = newX;
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%% Estimation
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% Estimated Parameters
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oo_.sur.dof = length(maxfp:minlp);
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[q, r] = qr(X, 0);
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xpxi = (r'*r)\eye(M_.param_nbr);
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resid = Y - X * (r\(q'*Y));
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resid = reshape(resid, oo_.sur.dof, length(lhs));
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M_.Sigma_e = resid'*resid/oo_.sur.dof;
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kLeye = kron(inv(M_.Sigma_e), eye(oo_.sur.dof));
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[q, r] = qr(kLeye*X, 0);
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oo_.sur.beta = r\(q'*kLeye*Y);
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M_.params(pidxs, 1) = oo_.sur.beta;
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% Yhat
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oo_.sur.Yhat = X * oo_.sur.beta;
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% Residuals
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oo_.sur.resid = Y - oo_.sur.Yhat;
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%% Calculate statistics
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% Estimate for sigma^2
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SS_res = oo_.sur.resid'*oo_.sur.resid;
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oo_.sur.s2 = SS_res/oo_.sur.dof;
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% R^2
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ym = Y - mean(Y);
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SS_tot = ym'*ym;
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oo_.sur.R2 = 1 - SS_res/SS_tot;
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% Adjusted R^2
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oo_.sur.adjR2 = oo_.sur.R2 - (1 - oo_.sur.R2)*M_.param_nbr/(oo_.sur.dof - 1);
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% Durbin-Watson
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ediff = oo_.sur.resid(2:oo_.sur.dof) - oo_.sur.resid(1:oo_.sur.dof - 1);
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oo_.sur.dw = (ediff'*ediff)/SS_res;
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% Standard Error
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oo_.sur.stderr = sqrt(oo_.sur.s2*diag(xpxi));
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% T-Stat
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oo_.sur.tstat = oo_.sur.beta./oo_.sur.stderr;
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%% Print Output
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if ~options_.noprint
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preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
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sprintf('No. Independent Variables: %d', M_.param_nbr), ...
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sprintf('Observations: %d', oo_.sur.dof)};
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afterward = {sprintf('R^2: %f', oo_.sur.R2), ...
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sprintf('R^2 Adjusted: %f', oo_.sur.adjR2), ...
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sprintf('s^2: %f', oo_.sur.s2), ...
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sprintf('Durbin-Watson: %f', oo_.sur.dw)};
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dyn_table('SUR Estimation', preamble, afterward, [vars{:}], ...
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{'Coefficients','t-statistic','Std. Error'}, 4, ...
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[oo_.sur.beta oo_.sur.tstat oo_.sur.stderr]);
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end
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end
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196
matlab/sur.m
196
matlab/sur.m
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@ -1,196 +0,0 @@
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function varargout = sur(ds, varargin)
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%function varargout = sur(ds, varargin)
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% Run a Seemingly Unrelated Regression on the provided equations
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%
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% INPUTS
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% ds [dseries] data
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%
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% OUTPUTS
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% varargout [cell array] contains the common work between sur and
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% surgibbs
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ oo_
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%% Check input
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assert(nargin == 1 || nargin == 3, 'Incorrect number of arguments passed to sur');
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jsonfile = [M_.fname '_original.json'];
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if exist(jsonfile, 'file') ~= 2
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error('Could not find %s! Please use the json option (See the Dynare invocation section in the reference manual).', jsonfile);
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end
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%% Get Equations
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jsonmodel = loadjson(jsonfile);
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jsonmodel = jsonmodel.model;
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[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, varargin{:});
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m = length(lhs);
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if m <= 1
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error('SUR estimation requires the selection of at least two equations')
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end
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%% Construct regression matrices
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Y = dseries();
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Xi = cell(m, 1);
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pnamesall = [];
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vwlagsall = [];
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for i = 1:m
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Y = [Y ds{lhs{i}}];
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rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','exp(','(',')'});
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rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
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vnames = setdiff(rhs_, cellstr(M_.param_names));
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regexprnoleads = cell2mat(strcat('(', vnames, {'\(\d+\))|'}));
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if ~isempty(regexp(rhs{i}, regexprnoleads(1:end-1), 'match'))
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error(['sur: you cannot have leads in equation on line ' ...
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lineno{i} ': ' lhs{i} ' = ' rhs{i}]);
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end
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regexpr = cell2mat(strcat('(', vnames, {'\(-\d+\))|'}));
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vwlags = regexp(rhs{i}, regexpr(1:end-1), 'match');
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% Find parameters
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pnames = cell(1, length(vwlags));
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for j = 1:length(vwlags)
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regexmatch = regexp(rhs{i}, ['(\w*\*?)?' strrep(strrep(vwlags{j}, '(', '\('), ')', '\)') '(\*?\w*)?'], 'match');
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regexmatch = strsplit(regexmatch{:}, '*');
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assert(length(regexmatch) == 2);
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if strcmp(vwlags{j}, regexmatch{1})
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pnames{j} = regexmatch{2};
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else
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pnames{j} = regexmatch{1};
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end
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end
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pnamesall = [pnamesall pnames];
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vwlagsall = [vwlagsall vwlags];
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Xi{i} = cellfun(@eval, strcat('ds.', vwlags), 'UniformOutput', false);
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end
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fp = Y.firstobservedperiod;
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lp = Y.lastobservedperiod;
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for i = 1:m
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X = dseries();
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for j = 1:length(Xi{i})
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X = [X dseries(Xi{i}{j}.data, Xi{i}{j}.dates, ['V' num2str(i) num2str(j)])];
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end
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Xi{i} = X;
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fp = max(fp, X.firstobservedperiod);
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lp = min(lp, X.lastobservedperiod);
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end
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Y = Y(fp:lp).data(:);
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X = [];
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for i = 1:m
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Xi{i} = Xi{i}(fp:lp).data;
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ind = size(X);
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X(ind(1)+1:ind(1)+size(Xi{i}, 1), ind(2)+1:ind(2)+size(Xi{i},2)) = Xi{i};
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end
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%% Estimation
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nobs = length(fp:lp);
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nvars = size(X, 2);
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[q, r] = qr(X, 0);
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xpxi = (r'*r)\eye(nvars);
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resid = Y - X * (r\(q'*Y));
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resid = reshape(resid, nobs, m);
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s2 = resid'*resid/nobs;
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tmp = kron(inv(s2), eye(nobs));
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beta = (X'*tmp*X)\X'*tmp*Y;
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% if called from surgibbs, return common work
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st = dbstack(1);
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if strcmp(st(1).name, 'surgibbs')
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varargout{1} = nobs;
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varargout{2} = nvars;
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varargout{3} = pnamesall;
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varargout{4} = beta;
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varargout{5} = X;
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varargout{6} = Y;
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varargout{7} = m;
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return
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end
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oo_.sur.s2 = s2;
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oo_.sur.beta = beta;
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for j = 1:length(pnamesall)
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M_.params(strmatch(pnamesall{j}, M_.param_names, 'exact')) = oo_.sur.beta(j);
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end
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% Yhat
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oo_.sur.Yhat = X * oo_.sur.beta;
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% Residuals
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oo_.sur.resid = Y - oo_.sur.Yhat;
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%% Calculate statistics
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oo_.sur.dof = nobs;
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% Estimate for sigma^2
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SS_res = oo_.sur.resid'*oo_.sur.resid;
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oo_.sur.s2 = SS_res/oo_.sur.dof;
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% R^2
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ym = Y - mean(Y);
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SS_tot = ym'*ym;
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oo_.sur.R2 = 1 - SS_res/SS_tot;
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% Adjusted R^2
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oo_.sur.adjR2 = oo_.sur.R2 - (1 - oo_.sur.R2)*nvars/(oo_.sur.dof-1);
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% Durbin-Watson
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ediff = oo_.sur.resid(2:nobs) - oo_.sur.resid(1:nobs-1);
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oo_.sur.dw = (ediff'*ediff)/SS_res;
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% Standard Error
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oo_.sur.stderr = sqrt(oo_.sur.s2*diag(xpxi));
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% T-Stat
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oo_.sur.tstat = oo_.sur.beta./oo_.sur.stderr;
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%% Print Output
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title = sprintf('SUR Estimation');
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if nargin == 1
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title = [title sprintf(' of all equations')];
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else
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title = [title s(' [%s = {', varargin{1})];
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for i = 1:length(varargin{2})
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if i ~= 1
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title = [title sprintf(', ')];
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end
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title = [title sprintf('%s', varargin{2}{i})];
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end
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title = [title sprintf('}]')];
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end
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preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
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sprintf('No. Independent Variables: %d', nvars), ...
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sprintf('Observations: %d', nobs)};
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afterward = {sprintf('R^2: %f', oo_.sur.R2), ...
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sprintf('R^2 Adjusted: %f', oo_.sur.adjR2), ...
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sprintf('s^2: %f', oo_.sur.s2), ...
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sprintf('Durbin-Watson: %f', oo_.sur.dw)};
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dyn_table(title, preamble, afterward, vwlagsall, ...
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{'Coefficients','t-statistic','Std. Error'}, 4, ...
|
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[oo_.sur.beta oo_.sur.tstat oo_.sur.stderr]);
|
||||
end
|
||||
|
|
@ -0,0 +1,151 @@
|
|||
// --+ options: json=compute +--
|
||||
|
||||
/* REMARK
|
||||
** ------
|
||||
**
|
||||
** You need to have the first line on top of the mod file. The options defined on this line are passed
|
||||
** to the dynare command (you can add other options, separated by spaces or commas). The option defined
|
||||
** here is mandatory for the decomposition. It forces Dynare to output another representation of the
|
||||
** model in JSON file (additionaly to the matlab files) which is used here to manipulate the equations.
|
||||
*/
|
||||
|
||||
var
|
||||
U2_Q_YED
|
||||
U2_G_YER
|
||||
U2_STN
|
||||
U2_ESTN
|
||||
U2_EHIC
|
||||
DE_Q_YED
|
||||
DE_G_YER
|
||||
DE_EHIC
|
||||
|
||||
;
|
||||
|
||||
varexo
|
||||
res_U2_Q_YED
|
||||
res_U2_G_YER
|
||||
res_U2_STN
|
||||
res_U2_ESTN
|
||||
res_U2_EHIC
|
||||
res_DE_Q_YED
|
||||
res_DE_G_YER
|
||||
res_DE_EHIC
|
||||
;
|
||||
|
||||
parameters
|
||||
u2_q_yed_ecm_u2_q_yed_L1
|
||||
u2_q_yed_ecm_u2_stn_L1
|
||||
u2_q_yed_u2_g_yer_L1
|
||||
u2_q_yed_u2_stn_L1
|
||||
u2_g_yer_ecm_u2_q_yed_L1
|
||||
u2_g_yer_ecm_u2_stn_L1
|
||||
u2_g_yer_u2_q_yed_L1
|
||||
u2_g_yer_u2_g_yer_L1
|
||||
u2_g_yer_u2_stn_L1
|
||||
u2_stn_ecm_u2_q_yed_L1
|
||||
u2_stn_ecm_u2_stn_L1
|
||||
u2_stn_u2_q_yed_L1
|
||||
u2_stn_u2_g_yer_L1
|
||||
u2_estn_u2_estn_L1
|
||||
u2_ehic_u2_ehic_L1
|
||||
|
||||
de_q_yed_ecm_de_q_yed_L1
|
||||
de_q_yed_ecm_u2_stn_L1
|
||||
de_q_yed_de_g_yer_L1
|
||||
de_q_yed_u2_stn_L1
|
||||
de_g_yer_ecm_de_q_yed_L1
|
||||
de_g_yer_ecm_u2_stn_L1
|
||||
de_g_yer_de_q_yed_L1
|
||||
de_g_yer_de_g_yer_L1
|
||||
de_g_yer_u2_stn_L1
|
||||
de_ehic_de_ehic_L1
|
||||
|
||||
|
||||
;
|
||||
|
||||
u2_q_yed_ecm_u2_q_yed_L1 = -0.82237516589315 ;
|
||||
u2_q_yed_ecm_u2_stn_L1 = -0.323715338568976 ;
|
||||
u2_q_yed_u2_g_yer_L1 = 0.0401361895021084 ;
|
||||
u2_q_yed_u2_stn_L1 = 0.058397703958446 ;
|
||||
u2_g_yer_ecm_u2_q_yed_L1 = 0.0189896046977421 ;
|
||||
u2_g_yer_ecm_u2_stn_L1 = -0.109597659887432 ;
|
||||
u2_g_yer_u2_q_yed_L1 = 0.0037667967632025 ;
|
||||
u2_g_yer_u2_g_yer_L1 = 0.480506381923644 ;
|
||||
u2_g_yer_u2_stn_L1 = -0.0722359286123494 ;
|
||||
u2_stn_ecm_u2_q_yed_L1 = -0.0438500662608356 ;
|
||||
u2_stn_ecm_u2_stn_L1 = -0.153283917138772 ;
|
||||
u2_stn_u2_q_yed_L1 = 0.0328744983772825 ;
|
||||
u2_stn_u2_g_yer_L1 = 0.292121949736756 ;
|
||||
u2_estn_u2_estn_L1 = 1 ;
|
||||
u2_ehic_u2_ehic_L1 = 1 ;
|
||||
|
||||
de_q_yed_ecm_de_q_yed_L1 = -0.822375165893149 ;
|
||||
de_q_yed_ecm_u2_stn_L1 = -0.323715338568977 ;
|
||||
de_q_yed_de_g_yer_L1 = 0.0401361895021082 ;
|
||||
de_q_yed_u2_stn_L1 = 0.0583977039584461 ;
|
||||
de_g_yer_ecm_de_q_yed_L1 = 0.0189896046977422 ;
|
||||
de_g_yer_ecm_u2_stn_L1 = -0.109597659887433 ;
|
||||
de_g_yer_de_q_yed_L1 = 0.00376679676320256;
|
||||
de_g_yer_de_g_yer_L1 = 0.480506381923643 ;
|
||||
de_g_yer_u2_stn_L1 = -0.0722359286123494 ;
|
||||
de_ehic_de_ehic_L1 = 1 ;
|
||||
|
||||
|
||||
model(linear);
|
||||
|
||||
diff(U2_Q_YED) = u2_q_yed_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
|
||||
+ u2_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ u2_q_yed_u2_g_yer_L1 * diff(U2_G_YER(-1))
|
||||
+ u2_q_yed_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_U2_Q_YED ;
|
||||
|
||||
diff(U2_G_YER) = u2_g_yer_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
|
||||
+ u2_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ u2_g_yer_u2_q_yed_L1 * diff(U2_Q_YED(-1))
|
||||
+ u2_g_yer_u2_g_yer_L1 * diff(U2_G_YER(-1))
|
||||
+ u2_g_yer_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_U2_G_YER ;
|
||||
|
||||
diff(U2_STN) = u2_stn_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
|
||||
+ u2_stn_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ u2_stn_u2_q_yed_L1 * diff(U2_Q_YED(-1))
|
||||
+ u2_stn_u2_g_yer_L1 * diff(U2_G_YER(-1))
|
||||
+ res_U2_STN ;
|
||||
|
||||
U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
|
||||
+ res_U2_ESTN ;
|
||||
|
||||
U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
|
||||
+ res_U2_EHIC ;
|
||||
|
||||
diff(DE_Q_YED) = de_q_yed_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
|
||||
+ de_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ de_q_yed_de_g_yer_L1 * diff(DE_G_YER(-1))
|
||||
+ de_q_yed_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_DE_Q_YED ;
|
||||
|
||||
diff(DE_G_YER) = de_g_yer_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
|
||||
+ de_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
|
||||
+ de_g_yer_de_q_yed_L1 * diff(DE_Q_YED(-1))
|
||||
+ de_g_yer_de_g_yer_L1 * diff(DE_G_YER(-1))
|
||||
+ de_g_yer_u2_stn_L1 * diff(U2_STN(-1))
|
||||
+ res_DE_G_YER ;
|
||||
|
||||
DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
|
||||
+ res_DE_EHIC ;
|
||||
|
||||
|
||||
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var res_U2_Q_YED = 0.005;
|
||||
var res_U2_G_YER = 0.005;
|
||||
var res_U2_STN = 0.005;
|
||||
var res_U2_ESTN = 0.005;
|
||||
var res_U2_EHIC = 0.005;
|
||||
var res_DE_Q_YED = 0.005;
|
||||
var res_DE_G_YER = 0.005;
|
||||
var res_DE_EHIC = 0.005;
|
||||
end;
|
||||
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
close all
|
||||
|
||||
dynare panel_var_diff_NB_simulation_test.mod;
|
||||
|
||||
NSIMS = 1000;
|
||||
|
||||
options_.noprint = 1;
|
||||
calibrated_values = M_.params;
|
||||
Sigma_e = M_.Sigma_e;
|
||||
|
||||
options_.bnlms.set_dynare_seed_to_default = false;
|
||||
|
||||
M_endo_names_trim = cellstr(M_.endo_names);
|
||||
nparampool = length(M_.params);
|
||||
BETA = zeros(NSIMS, nparampool);
|
||||
for i=1:NSIMS
|
||||
i
|
||||
firstobs = rand(3, length(M_endo_names_trim));
|
||||
M_.params = calibrated_values;
|
||||
M_.Sigma_e = Sigma_e;
|
||||
simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_endo_names_trim), 10000);
|
||||
simdata = simdata(simdata.dates(5001:6000));
|
||||
sur(simdata);
|
||||
BETA(i, :) = M_.params';
|
||||
end
|
||||
|
||||
mean(BETA)' - calibrated_values
|
||||
|
||||
for i=1:nparampool
|
||||
figure
|
||||
hold on
|
||||
title(strrep(M_.param_names(i,:), '_', '\_'));
|
||||
histogram(BETA(:,i),50);
|
||||
line([calibrated_values(i) calibrated_values(i)], [0 NSIMS/10], 'LineWidth', 2, 'Color', 'r');
|
||||
hold off
|
||||
end
|
||||
Loading…
Reference in New Issue