sur: fix up and use common code to create matrices
Houtan Bastani
parent
d8f26525b0
commit
f8c0282b01
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@ -1,5 +1,5 @@
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function [Y, lhssub, X, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconestrainedparams] = common_parsing(ds, ast, jsonmodel, overlapping_dates)
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%function [Y, lhssub, X, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = common_parsing(ds, ast, jsonmodel, overlapping_dates)
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function [Y, lhssub, X, startdates, enddates, startidxs, residnames, pbeta, vars] = common_parsing(ds, ast, jsonmodel, overlapping_dates)
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%function [Y, lhssub, X, startdates, enddates, startidxs, residnames, pbeta, vars] = common_parsing(ds, ast, jsonmodel, overlapping_dates)
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%
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% Code common to sur.m and pooled_ols.m
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%
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@ -84,6 +84,7 @@ end
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return
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%%
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@ -9,7 +9,7 @@ function [Y, lhssub, X] = parse_ols_style_equation(ds, ast, line)
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% line [int] equation line number
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%
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% OUTPUTS
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% Y [dseries] LHS of the equation
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% Y [dseries] LHS of the equation (with lhssub subtracted)
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% lhssub [dseries] RHS subtracted from LHS
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% X [dseries] RHS of the equation
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%
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@ -203,7 +203,7 @@ end
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function [param, X] = parseTimesNodeHelper(ds, node, line, param, X)
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if isOlsParamExpr(node, line)
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param = assignParam(param, node, line);
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elseif isOlsVarExpr(node, line)
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elseif isOlsVarExpr(ds, node, line)
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if isempty(X)
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X = evalNode(ds, node, line, X);
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else
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@ -237,7 +237,7 @@ else
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end
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end
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function tf = isOlsVar(node)
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function tf = isOlsVar(ds, node)
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if strcmp(node.node_type, 'VariableNode') ...
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&& (strcmp(node.type, 'endogenous') ...
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|| (strcmp(node.type, 'exogenous') && any(strcmp(ds.name, node.name))))
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@ -251,11 +251,11 @@ else
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end
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end
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function tf = isOlsVarExpr(node, line)
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function tf = isOlsVarExpr(ds, node, line)
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if strcmp(node.node_type, 'VariableNode') || strcmp(node.node_type, 'UnaryOpNode')
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tf = isOlsVar(node);
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tf = isOlsVar(ds, node);
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elseif strcmp(node.node_type, 'BinaryOpNode')
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tf = isOlsVarExpr(node.arg1, line) || isOlsVarExpr(node.arg2, line);
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tf = isOlsVarExpr(ds, node.arg1, line) || isOlsVarExpr(ds, node.arg2, line);
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else
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ols_error(['got unexpected type ' node.node_type], line);
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end
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@ -0,0 +1,67 @@
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function [Yvec, Xmat, constrained] = put_in_sur_form(Y, X)
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%function [Yvec, Xmat, constrained] = put_in_sur_form(Y, X)
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%
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% INPUTS
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% Y [cell array] dependent variables
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% X [cell array] regressors
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%
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% OUTPUTS
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% Yvec [vector] dependent variables
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% Xmat [matrix] regressors
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% constrained [cellstr] names of parameters that were constrained
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2019 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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%% Check inputs
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if nargin ~= 2
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error('put_in_sur_form expects 2 arguments');
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end
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if isempty(Y) || ~iscell(Y) || isempty(X) || ~iscell(X) || length(Y) ~= length(X)
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error('put_in_sur_form arguments should be cells of the same size');
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end
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%% Organize output
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nobs = size(X{1}, 1);
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neqs = length(X);
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Xmat = dseries([X{1}.data; zeros(nobs*(neqs-1), size(X{1}, 2))], X{1}.firstdate, X{1}.name);
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Yvec = Y{1};
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constrained = {};
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for i = 2:neqs
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to_remove = [];
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Xtmp = dseries([zeros(nobs*(i-1), size(X{i}, 2)); X{i}.data; zeros(nobs*(neqs-i), size(X{i}, 2))], X{i}.firstdate, X{i}.name);
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for j = 1:length(X{i}.name)
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idx = find(strcmp(Xmat.name, X{i}.name{j}));
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if ~isempty(idx)
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Xmat.(Xmat.name{idx}) = Xmat{idx} + Xtmp{j};
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to_remove = [to_remove j];
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constrained{end+1} = Xmat.name{idx};
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end
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end
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for j = length(to_remove):-1:1
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Xtmp = Xtmp.remove(Xtmp.name{j});
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end
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if ~isempty(Xtmp)
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Xmat = [Xmat Xtmp];
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end
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Yvec = dseries([Yvec.data; Y{i}.data], Yvec.firstdate);
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end
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end
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133
matlab/ols/sur.m
133
matlab/ols/sur.m
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@ -14,7 +14,7 @@ function varargout = sur(ds, param_names, eqtags)
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% SPECIAL REQUIREMENTS
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% dynare must be run with the option: json=compute
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% Copyright (C) 2017-2018 Dynare Team
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% Copyright (C) 2017-2019 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -34,113 +34,100 @@ function varargout = sur(ds, param_names, eqtags)
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global M_ oo_ options_
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%% Check input argument
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assert(nargin >= 1 && nargin <= 3, 'You must provide one, two, or three arguments');
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assert(~isempty(ds) && isdseries(ds), 'The first argument must be a dseries');
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if nargin >= 2
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assert(iscellstr(param_names), 'The 2nd argument must be a cellstr');
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else
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assert(nargin >= 1 && nargin <= 3, 'sur() takes between 1 and 3 arguments');
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if nargin < 3
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eqtags = {};
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end
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if nargin < 2
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param_names = {};
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else
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assert(iscellstr(param_names), 'sur: the 2nd argument must be a cellstr');
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end
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%% Read JSON
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jsonfile = [M_.fname filesep() 'model' filesep() 'json' filesep() 'modfile-original.json'];
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if exist(jsonfile, 'file') ~= 2
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error('Could not find %s! Please use the json=compute 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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if nargin == 3
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jsonmodel = getEquationsByTags(jsonmodel, 'name', eqtags);
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end
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%% Get Equation(s)
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[ast, jsonmodel] = get_ast_jsonmodel(eqtags);
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neqs = length(jsonmodel);
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%% Find parameters and variable names in equations and setup estimation matrices
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[X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, opidxs, surconstrainedparams] = ...
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pooled_sur_common(ds, jsonmodel);
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[Y, ~, X] = common_parsing(ds, ast, jsonmodel, true);
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clear ast jsonmodel;
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nobs = Y{1}.nobs;
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[Y, X, constrained] = put_in_sur_form(Y, X);
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if nargin == 1 && size(X, 2) ~= M_.param_nbr
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warning(['Not all parameters were used in model: ' ...
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sprintf('%s', strjoin(setdiff(M_.param_names, pbeta), ', '))]);
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warning(['Not all parameters were used in model: ' strjoin(setdiff(M_.param_names, X.name), ', ')]);
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end
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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(jsonmodel), 1);
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newX = zeros(nobs*length(jsonmodel), columns(X));
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lastidx = 1;
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for i = 1:length(jsonmodel)
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if i == length(jsonmodel)
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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(jsonmodel)
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lastidx = lastidx + nobs + 1;
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end
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% constrained_param_idxs: indexes in X.name of parameters that were constrained
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constrained_param_idxs = zeros(length(constrained), 1);
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for i = 1:length(constrained)
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constrained_param_idxs(i, 1) = find(strcmp(X.name, constrained{i}));
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end
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constrained_params_str = strjoin(X.name(constrained_param_idxs), ', ');
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if ~isempty(param_names)
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pnamesall = M_.param_names(opidxs);
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newX = dseries();
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nparams = length(param_names);
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pidxs = zeros(nparams, 1);
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for i = 1:nparams
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idxs = find(strcmp(param_names{i}, pnamesall));
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if isempty(idxs)
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idx = find(strcmp(param_names{i}, X.name));
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if isempty(idx)
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if ~isempty(eqtags)
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error(['Could not find ' param_names{i} ...
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' in the provided equations specified by ' strjoin(eqtags, ',')]);
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end
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error('Unspecified error. Please report');
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end
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pidxs(i) = idxs;
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pidxs(i) = idx;
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newX = [newX X.(X.name{idx})];
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end
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vars = [vars{:}];
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vars = {vars(pidxs)};
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newY = newY - newX(:, setdiff(1:size(newX, 2), pidxs)) * M_.params(setdiff(opidxs, opidxs(pidxs), 'stable'));
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newX = newX(:, pidxs);
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opidxs = opidxs(pidxs);
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subcols = setdiff(1:length(X.name), pidxs);
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for i = length(subcols):-1:1
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Y = Y - M_.params(strcmp(X.name{subcols(i)}, M_.param_names))*X.(X.name{subcols(i)});
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end
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X = newX;
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end
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% opidxs: indexes in M_.params associated with columns of X
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opidxs = zeros(length(X.name), 1);
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for i = 1:length(X.name)
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opidxs(i, 1) = find(strcmp(X.name{i}, M_.param_names));
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end
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%% Return to surgibbs if called from there
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st = dbstack(1);
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if strcmp(st(1).name, 'surgibbs')
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varargout{1} = length(maxfp:minlp); %dof
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varargout{1} = nobs; %dof
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varargout{2} = opidxs;
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varargout{3} = newX;
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varargout{4} = newY;
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varargout{5} = length(jsonmodel);
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varargout{3} = X.data;
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varargout{4} = Y.data;
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varargout{5} = neqs;
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return
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end
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Y = newY;
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X = newX;
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oo_.sur.dof = length(maxfp:minlp);
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%% Estimation
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oo_.sur.dof = nobs;
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% Estimated Parameters
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[q, r] = qr(X, 0);
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xpxi = (r'*r)\eye(size(X, 2));
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resid = Y - X * (r\(q'*Y));
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resid = reshape(resid, oo_.sur.dof, length(jsonmodel));
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[q, r] = qr(X.data, 0);
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xpxi = (r'*r)\eye(size(X.data, 2));
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resid = Y.data - X.data * (r\(q'*Y.data));
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resid = reshape(resid, oo_.sur.dof, neqs);
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M_.Sigma_e = resid'*resid/oo_.sur.dof;
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kLeye = kron(chol(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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[q, r] = qr(kLeye*X.data, 0);
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oo_.sur.beta = r\(q'*kLeye*Y.data);
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M_.params(opidxs) = oo_.sur.beta;
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% Yhat
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oo_.sur.Yhat = X * oo_.sur.beta;
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oo_.sur.Yhat = X.data * oo_.sur.beta;
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% Residuals
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oo_.sur.resid = Y - oo_.sur.Yhat;
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oo_.sur.resid = Y.data - oo_.sur.Yhat;
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%% Calculate statistics
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% Estimate for sigma^2
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@ -148,7 +135,7 @@ 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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ym = Y.data - mean(Y.data);
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SS_tot = ym'*ym;
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oo_.sur.R2 = 1 - SS_res/SS_tot;
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@ -167,7 +154,7 @@ 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('No. Equations: %d', length(jsonmodel)), ...
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preamble = {sprintf('No. Equations: %d', neqs), ...
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sprintf('No. Independent Variables: %d', size(X, 2)), ...
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sprintf('Observations: %d', oo_.sur.dof)};
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@ -176,14 +163,12 @@ if ~options_.noprint
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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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if ~isempty(surconstrainedparams)
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afterward = [afterward, ...
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sprintf('Constrained parameters: %s', ...
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strjoin(pbeta(surconstrainedparams), ', '))];
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if ~isempty(constrained_param_idxs)
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afterward = [afterward, ['Constrained parameters: ' constrained_params_str]];
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end
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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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dyn_table('SUR Estimation', preamble, afterward, X.name, ...
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{'Estimates','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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