dyn_ols: ols that allows for variables at time t on RHS
Houtan Bastani
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f07696ea5e
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b407f50753
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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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%
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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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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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[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, 'name', varargin{:});
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%% Estimation
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regexpr1 = ...
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['(diff\(\w+(\(\W?\w+\))?\))\*$' ...
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'|' '\((\w+(\(\W?\w+\))?(\W?\w+(\(\W?\w+\))?)*)\)\*$' ...
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'|' '(\w+(\(\W?\w+\))?)\*$' ...
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];
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regexpr2 = ...
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['^\*(diff\(\w+(\(\W?\w+\))?\))' ...
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'|' '^\*\((\w+(\(\W?\w+\))?(\W?\w+(\(\W?\w+\))?)*)' ...
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'|' '^\*(\w+(\(\W?\w+\))?)'
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];
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M_endo_names_trim = cellfun(@strtrim, num2cell(M_.endo_names(:,:),2), 'Uniform', 0);
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regex = ['(?<chb>^|(\-|\(|+|\*|\/|\^))(?<var>' ...
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strjoin(M_endo_names_trim, '|') ...
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')(?<cha>$|(\)\-|\(|+|\*|\/|\^))'];
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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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rhs_split = strsplit(rhs{i}, pnames{j});
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assert(length(rhs_split) == 2);
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if ~isempty(rhs_split{1}) && rhs_split{1}(end) == '*'
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tmp = regexp(rhs_split{1}, regexpr1, 'tokens');
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elseif ~isempty(rhs_split{2}) && rhs_split{2}(1) == '*'
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tmp = regexp(rhs_split{2}, regexpr2, 'tokens');
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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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vnames{j} = tmp{1}{:};
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Xtmp = getdata(ds, regex, vnames{j});
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Xtmp.rename_(vnames{j});
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X = [X Xtmp];
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end
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Y = ds{lhs{i}};
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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 = ['eqlineno' 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(strmatch(pnames{j}, M_.param_names, 'exact')) = 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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function retval = getdata(ds, regex, ser)
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if strncmp(ser, 'diff', 4)
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ser = ser(6:end-1);
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lagidx = strfind(ser, '(');
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if isempty(lagidx)
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retval = ds{ser} - ds{ser}(-1);
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else
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lag = str2double(ser(lagidx+1:strfind(ser, ')')-1));
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assert(lag < 0);
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ser = ser(1:lagidx-1);
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retval = ds{ser}(lag) - ds{ser}(lag-1);
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end
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else
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retval = eval(regexprep(ser, regex, '$<chb>ds.$<var>$<cha>'));
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end
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end
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