simplify getEquationsByTags
Houtan Bastani
parent
c17f648b39
commit
c42311de12
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@ -1,24 +1,23 @@
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function [lhs, rhs, linenum, sample, tagvalue] = getEquationsByTags(jsonmodel, varargin)
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%function [lhs, rhs, linenum, sample] = getEquationByTag(jsonmodel, varargin)
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% Return the lhs, rhs of an equation and the line it was defined
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% on given its tag
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function [jsonmodel] = getEquationsByTags(jsonmodel, tagname, tagvalue)
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%function [jsonmodel] = getEquationsByTags(jsonmodel, tagname, tagvalue)
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% Return the jsonmodel structure with the matching tags
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%
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% INPUTS
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% jsonmodel [string] JSON representation of model block
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% varargin [string or cellstring arrays] tagname and tagvalue for
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% eqs to get
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% jsonmodel [string] JSON representation of model block
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% tagname [string] The name of the tag whos values are to
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% be selected
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% tagvalue [string] The values to be selected for the
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% provided tagname
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%
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% OUTPUTS
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% lhs [cellstring array] left hand side of eq
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% rhs [cellstring array] right hand side of eq
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% linenum [cellstring array] eq line in .mod file
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% sample [cell array of dates] sample range
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% tagvalue [cellstring array] tags associated with equations
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% jsonmodel [string] JSON representation of model block,
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% with equations removed that don't match
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% eqtags
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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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% Copyright (C) 2017-2018 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -35,39 +34,9 @@ function [lhs, rhs, linenum, sample, tagvalue] = getEquationsByTags(jsonmodel, v
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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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assert(nargin == 1 || nargin == 3, 'Incorrect number of arguments passed to getEquationsByTags');
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if nargin == 1
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lhs = cell(1, length(jsonmodel));
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rhs = cell(1, length(jsonmodel));
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linenum = cell(1, length(jsonmodel));
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sample = cell(1, length(jsonmodel));
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tagvalue = cell(1, length(jsonmodel));
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for i=1:length(jsonmodel)
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lhs{i} = jsonmodel{i}.lhs;
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rhs{i} = jsonmodel{i}.rhs;
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linenum{i} = jsonmodel{i}.line;
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if isfield(jsonmodel{i}, 'tags') && ...
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isfield(jsonmodel{i}.tags, 'name')
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tagvalue{i} = jsonmodel{i}.tags.('name');
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else
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tagvalue{i} = ['eq_line_no_' num2str(linenum{i})];
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end
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if isfield(jsonmodel{i}, 'tags')
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if isfield(jsonmodel{i}.tags, 'sample')
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tmp = strsplit(jsonmodel{i}.tags.sample, ':');
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sample{i} = dates(tmp{1}):dates(tmp{2});
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end
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else
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tagvalue{i} = ['eq_line_no_' num2str(linenum{i})];
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end
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end
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return
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end
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tagname = varargin{1};
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tagvalue = varargin{2};
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assert(nargin == 3, 'Incorrect number of arguments passed to getEquationsByTags');
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assert(iscell(jsonmodel) && ~isempty(jsonmodel), ...
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'the first argument must be a cell array of structs');
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assert(ischar(tagname), 'Tag name must be a string');
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assert(ischar(tagvalue) || iscell(tagvalue), 'Tag value must be a string or a cell string array');
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@ -75,39 +44,22 @@ if ischar(tagvalue)
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tagvalue = {tagvalue};
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end
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lhs = cell(1, length(tagvalue));
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rhs = cell(1, length(tagvalue));
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linenum = cell(1, length(tagvalue));
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sample = cell(1, length(tagvalue));
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idx2rm = [];
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idx2keep = [];
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for j = 1:length(tagvalue)
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orig_len_idx2keep = length(idx2keep);
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for i=1:length(jsonmodel)
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assert(isstruct(jsonmodel{i}), 'Every entry in jsonmodel must be a struct');
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if isfield(jsonmodel{i}, 'tags') && ...
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isfield(jsonmodel{i}.tags, tagname) && ...
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strcmp(jsonmodel{i}.tags.(tagname), tagvalue{j})
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lhs{j} = jsonmodel{i}.lhs;
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rhs{j} = jsonmodel{i}.rhs;
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linenum{j} = jsonmodel{i}.line;
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if isfield(jsonmodel{i}.tags, 'sample')
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tmp = strsplit(jsonmodel{i}.tags.sample, ':');
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sample{j} = dates(tmp{1}):dates(tmp{2});
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end
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if ~any(cellfun(@isempty, lhs))
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return
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end
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idx2keep = [idx2keep; i];
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break
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end
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end
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if isempty(rhs{j})
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warning(['getEquationsByTags: No equation tag found by the name of ''' tagvalue{j} ''''])
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idx2rm = [idx2rm j];
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if length(idx2keep) == orig_len_idx2keep
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warning(['getEquationsByTags: no equation tag found by the name of ''' tagvalue{j} ''''])
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end
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end
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if ~isempty(idx2rm)
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lhs(:,idx2rm) = [];
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rhs(:,idx2rm) = [];
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linenum(:,idx2rm) = [];
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sample(:,idx2rm) = [];
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tagvalue(:,idx2rm) = [];
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assert(~isempty(idx2keep), 'getEquationsByTags: no equations selected');
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jsonmodel = jsonmodel(unique(idx2keep, 'stable'));
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end
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end
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@ -80,10 +80,10 @@ if exist(jsonfile, 'file') ~= 2
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end
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jsonmodel = loadjson(jsonfile);
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jsonmodel = jsonmodel.model;
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lhs = getEquationsByTags(jsonmodel, 'name', M_.var.(var_model_name).eqtags);
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jsonmodel = getEquationsByTags(jsonmodel, 'name', M_.var.(var_model_name).eqtags);
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lhsidxs = zeros(ntags, 1);
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for i = 1:ntags
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idxs = strcmp(M_.endo_names, lhs{i});
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idxs = strcmp(M_.endo_names, jsonmodel{i}.lhs);
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if any(idxs)
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lhsidxs(i) = find(idxs);
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continue
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@ -53,38 +53,30 @@ jsonmodel = loadjson(jsonfile);
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jsonmodel = jsonmodel.model;
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if nargin == 1
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[lhs, rhs, lineno, sample, tags] = getEquationsByTags(jsonmodel);
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fitted_names_dict = {};
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else
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elseif nargin == 2
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assert(isempty(fitted_names_dict) || ...
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(iscell(fitted_names_dict) && ...
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(size(fitted_names_dict, 2) == 2 || size(fitted_names_dict, 2) == 3)), ...
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'dyn_ols: the second argument must be an Nx2 or Nx3 cell array');
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if nargin == 2
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[lhs, rhs, lineno, sample, tags] = getEquationsByTags(jsonmodel);
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else
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[lhs, rhs, lineno, sample, tags] = getEquationsByTags(jsonmodel, 'name', eqtags);
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end
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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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elseif nargin == 3
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jsonmodel = getEquationsByTags(jsonmodel, 'name', eqtags);
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end
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%% Estimation
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M_endo_exo_names_trim = [M_.endo_names; M_.exo_names];
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regex = strjoin(M_endo_exo_names_trim(:,1), '|');
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mathops = '[\+\*\^\-\/\(\)]';
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for i = 1:length(lhs)
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for i = 1:length(jsonmodel)
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%% Construct regression matrices
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rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','exp(','(',')'});
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rhs_ = strsplit(jsonmodel{i}.rhs, {'+','-','*','/','^','log(','exp(','(',')'});
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rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
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vnames = setdiff(rhs_, M_.param_names);
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if ~isempty(regexp(rhs{i}, ...
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if ~isempty(regexp(jsonmodel{i}.rhs, ...
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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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jsonmodel{i}.line ': ' jsonmodel{i}.lhs ' = ' jsonmodel{i}.rhs]);
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end
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pnames = intersect(rhs_, M_.param_names);
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@ -98,27 +90,27 @@ for i = 1:length(lhs)
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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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[startidx, endidx] = regexp(jsonmodel{i}.rhs, pregex, 'start', 'end');
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assert(length(startidx) == 1);
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if rhs{i}(startidx) == '*' && rhs{i}(endidx) == '*'
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vnamesl = getStrMoveLeft(rhs{i}(1:startidx-1));
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vnamesr = getStrMoveRight(rhs{i}(endidx+1:end));
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if jsonmodel{i}.rhs(startidx) == '*' && jsonmodel{i}.rhs(endidx) == '*'
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vnamesl = getStrMoveLeft(jsonmodel{i}.rhs(1:startidx-1));
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vnamesr = getStrMoveRight(jsonmodel{i}.rhs(endidx+1:end));
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vnames{j} = [vnamesl '*' vnamesr];
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splitstrings{j} = [vnamesl '*' pnames{j} '*' vnamesr];
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elseif rhs{i}(startidx) == '*'
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vnames{j} = getStrMoveLeft(rhs{i}(1:startidx-1));
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elseif jsonmodel{i}.rhs(startidx) == '*'
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vnames{j} = getStrMoveLeft(jsonmodel{i}.rhs(1:startidx-1));
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splitstrings{j} = [vnames{j} '*' pnames{j}];
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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 jsonmodel{i}.rhs(endidx) == '*'
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vnames{j} = getStrMoveRight(jsonmodel{i}.rhs(endidx+1:end));
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splitstrings{j} = [pnames{j} '*' vnames{j}];
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if rhs{i}(startidx) == '-'
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if jsonmodel{i}.rhs(startidx) == '-'
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vnames{j} = ['-' vnames{j}];
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splitstrings{j} = ['-' splitstrings{j}];
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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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elseif jsonmodel{i}.rhs(startidx) == '+' ...
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|| jsonmodel{i}.rhs(startidx) == '-' ...
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|| jsonmodel{i}.rhs(endidx) == '+' ...
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|| jsonmodel{i}.rhs(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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@ -133,7 +125,7 @@ for i = 1:length(lhs)
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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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if rhs{i}(startidx) == '-'
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if jsonmodel{i}.rhs(startidx) == '-'
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Xtmp = dseries(-ones(ds.nobs, 1), ds.firstdate, vnames{j});
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else
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Xtmp = dseries(ones(ds.nobs, 1), ds.firstdate, vnames{j});
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@ -145,113 +137,120 @@ for i = 1:length(lhs)
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X = [X Xtmp];
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end
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lhssub = getRhsToSubFromLhs(ds, rhs{i}, regex, [splitstrings; pnames]);
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lhssub = getRhsToSubFromLhs(ds, jsonmodel{i}.rhs, regex, [splitstrings; pnames]);
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residuals = setdiff(intersect(rhs_, M_.exo_names), ds.name);
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assert(~isempty(residuals), ['No residuals in equation ' num2str(i)]);
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assert(length(residuals) == 1, ['More than one residual in equation ' num2str(i)]);
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Y = eval(regexprep(lhs{i}, regex, 'ds.$&'));
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Y = eval(regexprep(jsonmodel{i}.lhs, regex, 'ds.$&'));
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for j = 1:lhssub.vobs
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Y = Y - lhssub{j};
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end
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fp = max(Y.firstobservedperiod, X.firstobservedperiod);
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lp = min(Y.lastobservedperiod, X.lastobservedperiod);
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if ~isempty(sample{i})
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if fp > sample{i}(1) || lp < sample{i}(end)
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if isfield(jsonmodel{i}, 'sample') && ~isempty(jsonmodel{i}.sample)
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if fp > jsonmodel{i}.sample(1) || lp < jsonmodel{i}.sample(end)
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warning(['The sample over which you want to estimate contains NaNs. '...
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'Adjusting estimation range to be: ' fp.char ' to ' lp.char])
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else
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fp = sample{i}(1);
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lp = sample{i}(end);
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fp = jsonmodel{i}.sample(1);
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lp = jsonmodel{i}.sample(end);
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end
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end
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Y = Y(fp:lp);
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X = X(fp:lp).data;
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if isfield(jsonmodel{i}, 'tags') && ...
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isfield(jsonmodel{i}.tags, 'name')
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tag = jsonmodel{i}.tags.('name');
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else
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tag = ['eq_line_no_' num2str(jsonmodel{i}.line)];
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end
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%% Estimation
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% From LeSage, James P. "Applied Econometrics using MATLAB"
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[nobs, nvars] = size(X);
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oo_.ols.(tags{i}).dof = nobs - nvars;
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oo_.ols.(tag).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.(tags{i}).beta = r\(q'*Y.data);
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oo_.ols.(tag).beta = r\(q'*Y.data);
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for j = 1:length(pnames)
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M_.params(strcmp(M_.param_names, pnames{j})) = oo_.ols.(tags{i}).beta(j);
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M_.params(strcmp(M_.param_names, pnames{j})) = oo_.ols.(tag).beta(j);
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end
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% Yhat
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idx = 0;
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yhatname = [tags{i} '_FIT'];
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yhatname = [tag '_FIT'];
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if ~isempty(fitted_names_dict)
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idx = strcmp(fitted_names_dict(:,1), tags{i});
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idx = strcmp(fitted_names_dict(:,1), tag);
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if any(idx)
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yhatname = fitted_names_dict{idx, 2};
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end
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end
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oo_.ols.(tags{i}).Yhat = dseries(X*oo_.ols.(tags{i}).beta, fp, yhatname);
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oo_.ols.(tag).Yhat = dseries(X*oo_.ols.(tag).beta, fp, yhatname);
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if any(idx) ...
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&& length(fitted_names_dict(idx, :)) == 3 ...
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&& ~isempty(fitted_names_dict{idx, 3})
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oo_.ols.(tags{i}).Yhat = ...
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eval([fitted_names_dict{idx, 3} '(oo_.ols.(tags{' num2str(i) '}).Yhat)']);
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oo_.ols.(tag).Yhat = ...
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feval(fitted_names_dict{idx, 3}, oo_.ols.(tag).Yhat);
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end
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% Residuals
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oo_.ols.(tags{i}).resid = Y - oo_.ols.(tags{i}).Yhat;
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oo_.ols.(tag).resid = Y - oo_.ols.(tag).Yhat;
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% Correct Yhat reported back to user for given
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for j = 1:lhssub.vobs
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oo_.ols.(tags{i}).Yhat = oo_.ols.(tags{i}).Yhat + lhssub{j}(fp:lp);
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oo_.ols.(tag).Yhat = oo_.ols.(tag).Yhat + lhssub{j}(fp:lp);
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end
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ds = [ds oo_.ols.(tags{i}).Yhat];
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ds = [ds oo_.ols.(tag).Yhat];
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%% Calculate statistics
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% Estimate for sigma^2
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SS_res = oo_.ols.(tags{i}).resid.data'*oo_.ols.(tags{i}).resid.data;
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oo_.ols.(tags{i}).s2 = SS_res/oo_.ols.(tags{i}).dof;
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SS_res = oo_.ols.(tag).resid.data'*oo_.ols.(tag).resid.data;
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oo_.ols.(tag).s2 = SS_res/oo_.ols.(tag).dof;
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% R^2
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ym = Y.data - mean(Y);
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SS_tot = ym'*ym;
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oo_.ols.(tags{i}).R2 = 1 - SS_res/SS_tot;
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oo_.ols.(tag).R2 = 1 - SS_res/SS_tot;
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% Adjusted R^2
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oo_.ols.(tags{i}).adjR2 = oo_.ols.(tags{i}).R2 - (1 - oo_.ols.(tags{i}).R2)*nvars/(oo_.ols.(tags{i}).dof-1);
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oo_.ols.(tag).adjR2 = oo_.ols.(tag).R2 - (1 - oo_.ols.(tag).R2)*nvars/(oo_.ols.(tag).dof-1);
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% Durbin-Watson
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ediff = oo_.ols.(tags{i}).resid.data(2:nobs) - oo_.ols.(tags{i}).resid.data(1:nobs-1);
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oo_.ols.(tags{i}).dw = (ediff'*ediff)/SS_res;
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ediff = oo_.ols.(tag).resid.data(2:nobs) - oo_.ols.(tag).resid.data(1:nobs-1);
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oo_.ols.(tag).dw = (ediff'*ediff)/SS_res;
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% Standard Error
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oo_.ols.(tags{i}).stderr = sqrt(oo_.ols.(tags{i}).s2*diag(xpxi));
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oo_.ols.(tag).stderr = sqrt(oo_.ols.(tag).s2*diag(xpxi));
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% T-Stat
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oo_.ols.(tags{i}).tstat = oo_.ols.(tags{i}).beta./oo_.ols.(tags{i}).stderr;
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oo_.ols.(tag).tstat = oo_.ols.(tag).beta./oo_.ols.(tag).stderr;
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%% Print Output
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if ~options_.noprint
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if nargin == 3
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title = ['OLS Estimation of equation ''' tags{i} ''' [name = ''' tags{i} ''']'];
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title = ['OLS Estimation of equation ''' tag ''' [name = ''' tag ''']'];
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else
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title = ['OLS Estimation of equation ''' tags{i} ''''];
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title = ['OLS Estimation of equation ''' tag ''''];
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end
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preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
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preamble = {sprintf('Dependent Variable: %s', jsonmodel{i}.lhs), ...
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sprintf('No. Independent Variables: %d', nvars), ...
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sprintf('Observations: %d from %s to %s\n', nobs, fp.char, lp.char)};
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afterward = {sprintf('R^2: %f', oo_.ols.(tags{i}).R2), ...
|
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sprintf('R^2 Adjusted: %f', oo_.ols.(tags{i}).adjR2), ...
|
||||
sprintf('s^2: %f', oo_.ols.(tags{i}).s2), ...
|
||||
sprintf('Durbin-Watson: %f', oo_.ols.(tags{i}).dw)};
|
||||
afterward = {sprintf('R^2: %f', oo_.ols.(tag).R2), ...
|
||||
sprintf('R^2 Adjusted: %f', oo_.ols.(tag).adjR2), ...
|
||||
sprintf('s^2: %f', oo_.ols.(tag).s2), ...
|
||||
sprintf('Durbin-Watson: %f', oo_.ols.(tag).dw)};
|
||||
|
||||
dyn_table(title, preamble, afterward, vnames, ...
|
||||
{'Coefficients','t-statistic','Std. Error'}, 4, ...
|
||||
[oo_.ols.(tags{i}).beta oo_.ols.(tags{i}).tstat oo_.ols.(tags{i}).stderr]);
|
||||
[oo_.ols.(tag).beta oo_.ols.(tag).tstat oo_.ols.(tag).stderr]);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -58,13 +58,18 @@ for i = 1:length(param_regex)
|
|||
beta_idx = strcmp(pbeta, strrep(param_regex{i}, '*', oo_.pooled_fgls.country_name));
|
||||
assigned_idxs = assigned_idxs | beta_idx;
|
||||
value = oo_.pooled_fgls.beta(beta_idx);
|
||||
assert(~isempty(value));
|
||||
M_.params(~cellfun(@isempty, regexp(M_.param_names, ...
|
||||
strrep(param_regex{i}, '*', regexcountries)))) = value;
|
||||
if isempty(eqtags)
|
||||
assert(~isempty(value));
|
||||
end
|
||||
if ~isempty(value)
|
||||
M_.params(~cellfun(@isempty, regexp(M_.param_names, ...
|
||||
strrep(param_regex{i}, '*', regexcountries)))) = value;
|
||||
end
|
||||
end
|
||||
idxs = find(assigned_idxs == 0);
|
||||
values = oo_.pooled_fgls.beta(idxs);
|
||||
names = pbeta(idxs);
|
||||
assert(length(values) == length(names));
|
||||
for i = 1:length(idxs)
|
||||
M_.params(strcmp(M_.param_names, names{i})) = values(i);
|
||||
end
|
||||
|
|
|
|||
|
|
@ -70,9 +70,9 @@ end
|
|||
jsonmodel = loadjson(jsonfile);
|
||||
jsonmodel = jsonmodel.model;
|
||||
if nargin < 5
|
||||
[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
|
||||
eqtags ={};
|
||||
else
|
||||
[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, 'name', eqtags);
|
||||
jsonmodel = getEquationsByTags(jsonmodel, 'name', eqtags);
|
||||
end
|
||||
|
||||
%% Replace parameter names in equations
|
||||
|
|
@ -82,12 +82,14 @@ param_regex_idx = false(length(param_regex), 1);
|
|||
for i = 1:length(param_regex)
|
||||
splitp = strsplit(param_regex{i}, '*');
|
||||
assert(length(splitp) >= 2);
|
||||
rhstmp = regexprep(rhs, ...
|
||||
strjoin(splitp, regexcountries), ...
|
||||
strjoin(splitp, country_name));
|
||||
if length(intersect(rhs, rhstmp)) ~= length(rhs)
|
||||
rhs = rhstmp;
|
||||
param_regex_idx(i) = true;
|
||||
for j = 1:length(jsonmodel)
|
||||
rhstmp = regexprep(jsonmodel{j}.rhs, ...
|
||||
strjoin(splitp, regexcountries), ...
|
||||
strjoin(splitp, country_name));
|
||||
if length(intersect(jsonmodel{j}.rhs, rhstmp)) ~= length(jsonmodel{j}.rhs)
|
||||
jsonmodel{j}.rhs = rhstmp;
|
||||
param_regex_idx(i) = true;
|
||||
end
|
||||
end
|
||||
end
|
||||
param_regex = param_regex(param_regex_idx);
|
||||
|
|
@ -101,18 +103,18 @@ end
|
|||
|
||||
%% Find parameters and variable names in every equation & Setup estimation matrices
|
||||
[X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = ...
|
||||
pooled_sur_common(ds, lhs, rhs, lineno);
|
||||
pooled_sur_common(ds, jsonmodel);
|
||||
|
||||
if overlapping_dates
|
||||
maxfp = max([startdates{:}]);
|
||||
minlp = min([enddates{:}]);
|
||||
nobs = minlp - maxfp;
|
||||
newY = zeros(nobs*length(lhs), 1);
|
||||
newX = zeros(nobs*length(lhs), columns(X));
|
||||
newY = zeros(nobs*length(jsonmodel), 1);
|
||||
newX = zeros(nobs*length(jsonmodel), columns(X));
|
||||
newstartidxs = zeros(size(startidxs));
|
||||
newstartidxs(1) = 1;
|
||||
for i = 1:length(lhs)
|
||||
if i == length(lhs)
|
||||
for i = 1:length(jsonmodel)
|
||||
if i == length(jsonmodel)
|
||||
yds = dseries(Y(startidxs(i):end), startdates{i});
|
||||
xds = dseries(X(startidxs(i):end, :), startdates{i});
|
||||
else
|
||||
|
|
@ -121,7 +123,7 @@ if overlapping_dates
|
|||
end
|
||||
newY(newstartidxs(i):newstartidxs(i) + nobs, 1) = yds(maxfp:minlp).data;
|
||||
newX(newstartidxs(i):newstartidxs(i) + nobs, :) = xds(maxfp:minlp, :).data;
|
||||
if i ~= length(lhs)
|
||||
if i ~= length(jsonmodel)
|
||||
newstartidxs(i+1) = newstartidxs(i) + nobs + 1;
|
||||
end
|
||||
end
|
||||
|
|
@ -152,9 +154,13 @@ for i = 1:length(param_regex)
|
|||
beta_idx = strcmp(pbeta, strrep(param_regex{i}, '*', country_name));
|
||||
assigned_idxs = assigned_idxs | beta_idx;
|
||||
value = oo_.(save_structure_name).beta(beta_idx);
|
||||
assert(~isempty(value));
|
||||
M_.params(~cellfun(@isempty, regexp(M_.param_names, ...
|
||||
strrep(param_regex{i}, '*', regexcountries)))) = value;
|
||||
if isempty(eqtags)
|
||||
assert(~isempty(value));
|
||||
end
|
||||
if ~isempty(value)
|
||||
M_.params(~cellfun(@isempty, regexp(M_.param_names, ...
|
||||
strrep(param_regex{i}, '*', regexcountries)))) = value;
|
||||
end
|
||||
end
|
||||
idxs = find(assigned_idxs == 0);
|
||||
values = oo_.(save_structure_name).beta(idxs);
|
||||
|
|
@ -165,8 +171,8 @@ for i = 1:length(idxs)
|
|||
end
|
||||
|
||||
residuals = Y - X * oo_.(save_structure_name).beta;
|
||||
for i = 1:length(lhs)
|
||||
if i == length(lhs)
|
||||
for i = 1:length(jsonmodel)
|
||||
if i == length(jsonmodel)
|
||||
oo_.(save_structure_name).resid.(residnames{i}{:}) = residuals(startidxs(i):end);
|
||||
else
|
||||
oo_.(save_structure_name).resid.(residnames{i}{:}) = residuals(startidxs(i):startidxs(i+1)-1);
|
||||
|
|
|
|||
|
|
@ -1,13 +1,11 @@
|
|||
function [X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = pooled_sur_common(ds, lhs, rhs, lineno)
|
||||
%function [X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = pooled_sur_common(ds, lhs, rhs, lineno)
|
||||
function [X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = pooled_sur_common(ds, jsonmodel)
|
||||
%function [X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, surpidxs, surconstrainedparams] = pooled_sur_common(ds, jsonmodel)
|
||||
%
|
||||
% Code common to sur.m and pooled_ols.m
|
||||
%
|
||||
% INPUTS
|
||||
% ds [dseries] dataset
|
||||
% lhs [cellstr] LHS of equations
|
||||
% rhs [cellstr] RHS of equations
|
||||
% lineno [cellstr] line number of equations
|
||||
% jsonmodel [string] JSON representation of model block
|
||||
%
|
||||
% OUTPUTS
|
||||
% X [matrix] regressors
|
||||
|
|
@ -53,26 +51,26 @@ global M_
|
|||
M_endo_exo_names_trim = [M_.endo_names; M_.exo_names];
|
||||
regex = strjoin(M_endo_exo_names_trim(:,1), '|');
|
||||
mathops = '[\+\*\^\-\/]';
|
||||
params = cell(length(rhs),1);
|
||||
vars = cell(length(rhs),1);
|
||||
params = cell(length(jsonmodel),1);
|
||||
vars = cell(length(jsonmodel),1);
|
||||
pbeta = {};
|
||||
Y = [];
|
||||
X = [];
|
||||
startidxs = zeros(length(lhs), 1);
|
||||
startdates = cell(length(lhs), 1);
|
||||
enddates = cell(length(lhs), 1);
|
||||
residnames = cell(length(lhs), 1);
|
||||
startidxs = zeros(length(jsonmodel), 1);
|
||||
startdates = cell(length(jsonmodel), 1);
|
||||
enddates = cell(length(jsonmodel), 1);
|
||||
residnames = cell(length(jsonmodel), 1);
|
||||
surpidxs = zeros(M_.param_nbr, 1);
|
||||
surpidx = 0;
|
||||
surconstrainedparams = [];
|
||||
for i = 1:length(lhs)
|
||||
rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','ln(','log10(','exp(','(',')','diff('});
|
||||
for i = 1:length(jsonmodel)
|
||||
rhs_ = strsplit(jsonmodel{i}.rhs, {'+','-','*','/','^','log(','ln(','log10(','exp(','(',')','diff('});
|
||||
rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
|
||||
vnames = setdiff(rhs_, M_.param_names);
|
||||
if ~isempty(regexp(rhs{i}, ...
|
||||
if ~isempty(regexp(jsonmodel{i}.rhs, ...
|
||||
['(' strjoin(vnames, '\\(\\d+\\)|') '\\(\\d+\\))'], 'once'))
|
||||
error(['pooled_ols: you cannot have leads in equation on line ' ...
|
||||
lineno{i} ': ' lhs{i} ' = ' rhs{i}]);
|
||||
jsonmodel{i}.line ': ' jsonmodel{i}.lhs ' = ' jsonmodel{i}.rhs]);
|
||||
end
|
||||
|
||||
% Find parameters and associated variables
|
||||
|
|
@ -101,25 +99,25 @@ for i = 1:length(lhs)
|
|||
'|^' pnames{j} mathops ...
|
||||
'|' mathops pnames{j} '$' ...
|
||||
];
|
||||
[startidx, endidx] = regexp(rhs{i}, pregex, 'start', 'end');
|
||||
[startidx, endidx] = regexp(jsonmodel{i}.rhs, pregex, 'start', 'end');
|
||||
assert(length(startidx) == 1);
|
||||
if rhs{i}(startidx) == '*' && rhs{i}(endidx) == '*'
|
||||
vnames{j} = [getStrMoveLeft(rhs{i}(1:startidx-1)) '*' ...
|
||||
getStrMoveRight(rhs{i}(endidx+1:end))];
|
||||
elseif rhs{i}(startidx) == '*'
|
||||
vnames{j} = getStrMoveLeft(rhs{i}(1:startidx-1));
|
||||
if jsonmodel{i}.rhs(startidx) == '*' && jsonmodel{i}.rhs(endidx) == '*'
|
||||
vnames{j} = [getStrMoveLeft(jsonmodel{i}.rhs(1:startidx-1)) '*' ...
|
||||
getStrMoveRight(jsonmodel{i}.rhs(endidx+1:end))];
|
||||
elseif jsonmodel{i}.rhs(startidx) == '*'
|
||||
vnames{j} = getStrMoveLeft(jsonmodel{i}.rhs(1:startidx-1));
|
||||
splitstrings{j} = [vnames{j} '*' pnames{j}];
|
||||
elseif rhs{i}(endidx) == '*'
|
||||
vnames{j} = getStrMoveRight(rhs{i}(endidx+1:end));
|
||||
elseif jsonmodel{i}.rhs(endidx) == '*'
|
||||
vnames{j} = getStrMoveRight(jsonmodel{i}.rhs(endidx+1:end));
|
||||
splitstrings{j} = [pnames{j} '*' vnames{j}];
|
||||
if rhs{i}(startidx) == '-'
|
||||
if jsonmodel{i}.rhs(startidx) == '-'
|
||||
vnames{j} = ['-' vnames{j}];
|
||||
splitstrings{j} = ['-' splitstrings{j}];
|
||||
end
|
||||
elseif rhs{i}(startidx) == '+' ...
|
||||
|| rhs{i}(startidx) == '-' ...
|
||||
|| rhs{i}(endidx) == '+' ...
|
||||
|| rhs{i}(endidx) == '-'
|
||||
elseif jsonmodel{i}.rhs(startidx) == '+' ...
|
||||
|| jsonmodel{i}.rhs(startidx) == '-' ...
|
||||
|| jsonmodel{i}.rhs(endidx) == '+' ...
|
||||
|| jsonmodel{i}.rhs(endidx) == '-'
|
||||
% intercept
|
||||
createdvar = true;
|
||||
if any(strcmp(M_endo_exo_names_trim, 'intercept'))
|
||||
|
|
@ -146,7 +144,7 @@ for i = 1:length(lhs)
|
|||
vnames = vnames(dropvname);
|
||||
end
|
||||
|
||||
lhssub = getRhsToSubFromLhs(ds, rhs{i}, regex, [splitstrings; pnames]);
|
||||
lhssub = getRhsToSubFromLhs(ds, jsonmodel{i}.rhs, regex, [splitstrings; pnames]);
|
||||
|
||||
residnames{i} = setdiff(intersect(rhs_, M_.exo_names), ds.name);
|
||||
assert(~isempty(residnames{i}), ['No residuals in equation ' num2str(i)]);
|
||||
|
|
@ -155,7 +153,7 @@ for i = 1:length(lhs)
|
|||
params{i} = pnames;
|
||||
vars{i} = vnames;
|
||||
|
||||
ydata = eval(regexprep(lhs{i}, regex, 'ds.$&'));
|
||||
ydata = eval(regexprep(jsonmodel{i}.lhs, regex, 'ds.$&'));
|
||||
for j = 1:lhssub.vobs
|
||||
ydata = ydata - lhssub{j};
|
||||
end
|
||||
|
|
|
|||
|
|
@ -44,15 +44,13 @@ end
|
|||
|
||||
jsonmodel = loadjson(jsonfile);
|
||||
jsonmodel = jsonmodel.model;
|
||||
if nargin == 1
|
||||
[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
|
||||
else
|
||||
[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, 'name', eqtags);
|
||||
if nargin == 2
|
||||
jsonmodel = getEquationsByTags(jsonmodel, 'name', eqtags);
|
||||
end
|
||||
|
||||
%% Find parameters and variable names in equations and setup estimation matrices
|
||||
[X, Y, startdates, enddates, startidxs, residnames, pbeta, vars, pidxs, surconstrainedparams] = ...
|
||||
pooled_sur_common(ds, lhs, rhs, lineno);
|
||||
pooled_sur_common(ds, jsonmodel);
|
||||
|
||||
if nargin == 1 && size(X, 2) ~= M_.param_nbr
|
||||
warning(['Not all parameters were used in model: ' ...
|
||||
|
|
@ -63,11 +61,11 @@ end
|
|||
maxfp = max([startdates{:}]);
|
||||
minlp = min([enddates{:}]);
|
||||
nobs = minlp - maxfp;
|
||||
newY = zeros(nobs*length(lhs), 1);
|
||||
newX = zeros(nobs*length(lhs), columns(X));
|
||||
newY = zeros(nobs*length(jsonmodel), 1);
|
||||
newX = zeros(nobs*length(jsonmodel), columns(X));
|
||||
lastidx = 1;
|
||||
for i = 1:length(lhs)
|
||||
if i == length(lhs)
|
||||
for i = 1:length(jsonmodel)
|
||||
if i == length(jsonmodel)
|
||||
yds = dseries(Y(startidxs(i):end), startdates{i});
|
||||
xds = dseries(X(startidxs(i):end, :), startdates{i});
|
||||
else
|
||||
|
|
@ -76,7 +74,7 @@ for i = 1:length(lhs)
|
|||
end
|
||||
newY(lastidx:lastidx + nobs, 1) = yds(maxfp:minlp).data;
|
||||
newX(lastidx:lastidx + nobs, :) = xds(maxfp:minlp, :).data;
|
||||
if i ~= length(lhs)
|
||||
if i ~= length(jsonmodel)
|
||||
lastidx = lastidx + nobs + 1;
|
||||
end
|
||||
end
|
||||
|
|
@ -88,7 +86,7 @@ if strcmp(st(1).name, 'surgibbs')
|
|||
varargout{2} = pidxs;
|
||||
varargout{3} = newX;
|
||||
varargout{4} = newY;
|
||||
varargout{5} = length(lhs);
|
||||
varargout{5} = length(jsonmodel);
|
||||
return
|
||||
end
|
||||
|
||||
|
|
@ -101,7 +99,7 @@ oo_.sur.dof = length(maxfp:minlp);
|
|||
[q, r] = qr(X, 0);
|
||||
xpxi = (r'*r)\eye(size(X, 2));
|
||||
resid = Y - X * (r\(q'*Y));
|
||||
resid = reshape(resid, oo_.sur.dof, length(lhs));
|
||||
resid = reshape(resid, oo_.sur.dof, length(jsonmodel));
|
||||
|
||||
M_.Sigma_e = resid'*resid/oo_.sur.dof;
|
||||
kLeye = kron(chol(inv(M_.Sigma_e)), eye(oo_.sur.dof));
|
||||
|
|
@ -141,7 +139,7 @@ oo_.sur.tstat = oo_.sur.beta./oo_.sur.stderr;
|
|||
|
||||
%% Print Output
|
||||
if ~options_.noprint
|
||||
preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
|
||||
preamble = {sprintf('Dependent Variable: %s', jsonmodel{i}.lhs), ...
|
||||
sprintf('No. Independent Variables: %d', M_.param_nbr), ...
|
||||
sprintf('Observations: %d', oo_.sur.dof)};
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue