194 lines
6.3 KiB
Matlab
194 lines
6.3 KiB
Matlab
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function dyn_ols(ds, varargin)
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% function dyn_ols(ds, varargin)
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% Run OLS on chosen model equations; unlike olseqs, allow for time t
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% endogenous variables on LHS
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%
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% INPUTS
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% ds [dseries] data
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% varargin [cellstr] names of equation tags to estimate. If empty,
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% estimate all equations
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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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% 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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assert(nargin <= 2, 'Incorrect number of arguments.');
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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 Equation(s)
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jsonmodel = loadjson(jsonfile);
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jsonmodel = jsonmodel.model;
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if nargin == 1
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[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
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else
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[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, 'name', varargin{:});
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if isempty(lhs)
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disp('dyn_ols: Nothing to estimate')
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return
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end
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end
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%% Estimation
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M_endo_exo_names_trim = cellfun(@strtrim, ...
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[num2cell(M_.endo_names(:,:),2) ; num2cell(M_.exo_names(:,:),2)], ...
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'Uniform', 0);
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regex = strjoin(M_endo_exo_names_trim(:,1), '|');
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mathops = '[\+\*\^\-\/]';
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M_param_names_trim = cellfun(@strtrim, num2cell(M_.param_names,2), 'UniformOutput', false);
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for i = 1:length(lhs)
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%% Construct regression matrices
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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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if ~isempty(regexp(rhs{i}, ...
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['(' strjoin(vnames, '\\(\\d+\\)|') '\\(\\d+\\))'], ...
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'once'))
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error(['dyn_ols: 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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pnames = intersect(rhs_, cellstr(M_.param_names));
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vnames = cell(1, length(pnames));
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X = dseries();
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for j = 1:length(pnames)
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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('dyn_ols: Shouldn''t arrive here');
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end
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if createdvar
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Xtmp = dseries(ones(ds.nobs, 1), ds.firstdate, vnames{j});
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else
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Xtmp = eval(regexprep(vnames{j}, regex, 'ds.$&'));
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Xtmp.rename_(vnames{j});
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end
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X = [X Xtmp];
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end
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Y = eval(regexprep(lhs{i}, regex, 'ds.$&'));
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fp = max(Y.firstobservedperiod, X.firstobservedperiod);
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lp = min(Y.lastobservedperiod, X.lastobservedperiod);
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Y = Y(fp:lp).data;
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X = X(fp:lp).data;
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%% Estimation
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% From LeSage, James P. "Applied Econometrics using MATLAB"
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if nargin == 2
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if iscell(varargin{1})
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tagv = varargin{1}{i};
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else
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tagv = varargin{1};
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end
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else
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tagv = ['eq_line_no_' num2str(lineno{i})];
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end
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[nobs, nvars] = size(X);
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oo_.ols.(tagv).dof = nobs - nvars;
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% Estimated Parameters
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[q, r] = qr(X, 0);
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xpxi = (r'*r)\eye(nvars);
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oo_.ols.(tagv).beta = r\(q'*Y);
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for j = 1:length(pnames)
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M_.params(strcmp(M_param_names_trim, pnames{j})) = oo_.ols.(tagv).beta(j);
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end
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% Yhat
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oo_.ols.(tagv).Yhat = X*oo_.ols.(tagv).beta;
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% Residuals
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oo_.ols.(tagv).resid = Y - oo_.ols.(tagv).Yhat;
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%% Calculate statistics
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% Estimate for sigma^2
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SS_res = oo_.ols.(tagv).resid'*oo_.ols.(tagv).resid;
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oo_.ols.(tagv).s2 = SS_res/oo_.ols.(tagv).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_.ols.(tagv).R2 = 1 - SS_res/SS_tot;
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% Adjusted R^2
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oo_.ols.(tagv).adjR2 = oo_.ols.(tagv).R2 - (1 - oo_.ols.(tagv).R2)*nvars/(oo_.ols.(tagv).dof-1);
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% Durbin-Watson
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ediff = oo_.ols.(tagv).resid(2:nobs) - oo_.ols.(tagv).resid(1:nobs-1);
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oo_.ols.(tagv).dw = (ediff'*ediff)/SS_res;
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% Standard Error
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oo_.ols.(tagv).stderr = sqrt(oo_.ols.(tagv).s2*diag(xpxi));
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% T-Stat
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oo_.ols.(tagv).tstat = oo_.ols.(tagv).beta./oo_.ols.(tagv).stderr;
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%% Print Output
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title = sprintf('OLS Estimation of equation `%s`', tagv);
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if nargin == 3
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title = [title sprintf(' [%s = %s]', 'name', tagv)];
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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_.ols.(tagv).R2), ...
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sprintf('R^2 Adjusted: %f', oo_.ols.(tagv).adjR2), ...
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sprintf('s^2: %f', oo_.ols.(tagv).s2), ...
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sprintf('Durbin-Watson: %f', oo_.ols.(tagv).dw)};
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dyn_table(title, preamble, afterward, vnames, ...
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{'Coefficients','t-statistic','Std. Error'}, 4, ...
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[oo_.ols.(tagv).beta oo_.ols.(tagv).tstat oo_.ols.(tagv).stderr]);
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end
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end
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