ols: run multiple estimations when more than one tag is passed
Houtan Bastani
parent
cc121aa660
commit
07fb484987
205
matlab/olseqs.m
205
matlab/olseqs.m
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@ -42,109 +42,112 @@ end
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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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lhs = lhs{:};
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rhs = rhs{:};
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lineno = lineno{:};
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nols = length(lhs);
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%% Construct regression matrices
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Y = ds{lhs}.data;
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for i = 1:nols
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%% Construct regression matrices
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Y = ds{lhs{i}}.data;
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rhs_ = strsplit(rhs, {'+','-','*','/','^','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, regexprnoleads(1:end-1), 'match'))
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error(['olseqs: you cannot have leads in equation on line ' lineno ': ' lhs ' = ' rhs]);
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end
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regexpr = cell2mat(strcat('(', vnames, {'\(-\d+\))|'}));
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vwlags = regexp(rhs, 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 i = 1:length(vwlags)
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regexmatch = regexp(rhs, ['\w*\*?' strrep(strrep(vwlags{i}, '(', '\('), ')', '\)')], 'match');
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regexmatch = strsplit(regexmatch{:}, '*');
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pnames{i} = regexmatch{1};
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end
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X = cell2mat(cellfun(@eval, strcat('ds.', vwlags, '.data'), 'UniformOutput', false));
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% Remove all rows that have a NaN
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[row, ~] = find(isnan(X), 1, 'last');
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Y = Y(row+1:end, :);
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X = X(row+1:end, :);
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% Add intercept
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% X = [ones(size(X,1), 1), X];
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%% OLS Estimation
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% From LeSage, James P. "Applied Econometrics using MATLAB"
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tagv = varargin{2};
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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 i = 1:length(pnames)
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M_.params(strmatch(pnames{i}, M_.param_names, 'exact')) = oo_.ols.(tagv).beta(i);
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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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% 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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fprintf('OLS Estimation of equation on line %d of %s\n', lineno, [M_.fname '.mod']);
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fprintf('Dependent Variable: %s\n', lhs);
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fprintf('No. Independent Variables: %d\n', nvars);
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fprintf('Observations: %d\n', nobs);
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maxstrlen = 0;
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for i=1:length(vwlags)
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slen = length(vwlags{i});
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if slen > maxstrlen
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maxstrlen = slen;
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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(['olseqs: 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}, '(', '\('), ')', '\)')], 'match');
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regexmatch = strsplit(regexmatch{:}, '*');
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pnames{j} = regexmatch{1};
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end
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X = cell2mat(cellfun(@eval, strcat('ds.', vwlags, '.data'), 'UniformOutput', false));
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% Remove all rows that have a NaN
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[row, ~] = find(isnan(X), 1, 'last');
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Y = Y(row+1:end, :);
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X = X(row+1:end, :);
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%% Estimation
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% From LeSage, James P. "Applied Econometrics using MATLAB"
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if iscell(varargin{2})
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tagv = varargin{2}{i};
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else
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tagv = varargin{2};
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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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fprintf('OLS Estimation of equation on line %d of %s\n', lineno{i}, [M_.fname '.mod']);
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fprintf('Dependent Variable: %s\n', lhs{i});
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fprintf('No. Independent Variables: %d\n', nvars);
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fprintf('Observations: %d\n', nobs);
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maxstrlen = 0;
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for i=1:length(vwlags)
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slen = length(vwlags{i});
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if slen > maxstrlen
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maxstrlen = slen;
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end
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end
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titlespacing = repmat(' ', 1, 4 + maxstrlen + 4) ;
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fprintf('%sCoefficients t-statistic Std. Error\n', titlespacing);
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fprintf('%s____________ ____________ ____________\n\n', titlespacing);
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format = [' %-' num2str(maxstrlen) 's'];
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for i = 1:length(vwlags)
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fprintf(format, vwlags{i});
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fprintf('%12.5f %12.5f %12.5f\n', ...
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oo_.ols.(tagv).beta(i), ...
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oo_.ols.(tagv).tstat(i), ...
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oo_.ols.(tagv).stderr(i));
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end
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fprintf('\nR^2: %f\n', oo_.ols.(tagv).R2);
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fprintf('R^2 Adjusted: %f\n', oo_.ols.(tagv).adjR2);
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fprintf('s^2: %f\n', oo_.ols.(tagv).s2);
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fprintf('Durbin-Watson: %f\n', oo_.ols.(tagv).dw);
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fprintf('%s\n', repmat('-', 1, 4 + maxstrlen + 4 + 44));
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end
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titlespacing = repmat(' ', 1, 4 + maxstrlen + 4) ;
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fprintf('%sCoefficients t-statistic Std. Error\n', titlespacing);
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fprintf('%s____________ ____________ ____________\n\n', titlespacing);
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format = [' %-' num2str(maxstrlen) 's'];
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for i = 1:length(vwlags)
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fprintf(format, vwlags{i});
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fprintf('%12.5f %12.5f %12.5f\n', ...
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oo_.ols.(tagv).beta(i), ...
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oo_.ols.(tagv).tstat(i), ...
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oo_.ols.(tagv).stderr(i));
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end
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fprintf('\nR^2: %f\n', oo_.ols.(tagv).R2);
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fprintf('R^2 Adjusted: %f\n', oo_.ols.(tagv).adjR2);
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fprintf('s^2: %f\n', oo_.ols.(tagv).s2);
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fprintf('Durbin-Watson: %f\n', oo_.ols.(tagv).dw);
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fprintf('%s\n', repmat('-', 1, 4 + maxstrlen + 4 + 44));
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end
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@ -11,7 +11,7 @@ model;
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unrate = adl(unrate, 'p_ffr_unrate', [4 2 5]) + adl(cpi, 'p_unrate_cpi', 6);
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[eqnum='cpi']
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cpi = adl(ffr, 'p_cpi_ffr', 6) + adl(cpi, 'p_cpi_cpi', 6);
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cpi = adl(ffr, 'p_cpi_ffr', 2) + adl(cpi, 'p_cpi_cpi', [2]);
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end;
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@ -28,6 +28,6 @@ ds1 = dseries(randn(30, 3), 1, {'ffr', 'unrate', 'cpi'});
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// Baseline paths for the variables.
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ds0 = dseries(zeros(30, 3), 1, {'ffr', 'unrate', 'cpi'});
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olseqs(ds1, 'eqnum', 'ffr');
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olseqs(ds1, 'eqnum', {'ffr', 'cpi'});
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plot_contributions('eqnum', 'ffr', ds1, ds0);
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