128 lines
4.2 KiB
Matlab
128 lines
4.2 KiB
Matlab
function get_ar_matrices(var_model_name)
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%function ar = get_ar_matrices(var_model_name)
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% Gets the autoregressive matrices associated with the var specified by
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% var_model_name. Output stored in cellarray oo_.var.(var_model_name).ar,
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% with oo_.var.(var_model_name).ar(1) being the AR matrix at time t,
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% oo_.var.(var_model_name).ar(2) the AR matrix at time t-1, etc. Each
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% AR matrix is stored with rows organized by the ordering of the equation
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% tags found in M_.var.(var_model_name).eqtags and columns organized by
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% M_.endo_names order
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%
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% INPUTS
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%
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% var_model_name [string] the name of the VAR model
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%
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% OUTPUTS
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%
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% NONE
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% Copyright (C) 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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% 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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%% Check inputs and initialize output
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assert(nargin == 1, 'This function requires one argument');
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assert(~isempty(var_model_name) && ischar(var_model_name), ...
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'The sole argument must be a non-empty string');
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if ~isfield(M_.var, var_model_name)
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error(['Could not find ' var_model_name ' in M_.var. ' ...
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'First declare it via the var_model statement.']);
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end
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%% Call Dynamic Function
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[junk, g1] = feval([M_.fname '_dynamic'], ...
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ones(max(max(M_.lead_lag_incidence)), 1), ...
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ones(1, M_.exo_nbr), ...
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M_.params, ...
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zeros(M_.endo_nbr, 1), ...
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1);
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% Choose rows of Jacobian based on equation tags
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ntags = length(M_.var.(var_model_name).eqtags);
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g1rows = zeros(ntags, 1);
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for i = 1:ntags
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idxs = strcmp(M_.equations_tags(:, 3), M_.var.(var_model_name).eqtags{i});
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if any(idxs)
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g1rows(i) = M_.equations_tags{idxs, 1};
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continue
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end
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end
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g1 = -1 * g1(g1rows, :);
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% Check for leads
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if rows(M_.lead_lag_incidence) == 3
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idxs = M_.lead_lag_incidence(3, M_.lead_lag_incidence(3, :) ~= 0);
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assert(~any(g1(:, idxs)), ...
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['You cannot have leads in the equations specified by ' strjoin(M_.var.(var_model_name).eqtags, ',')]);
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end
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%% Organize AR matrices
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% Get LHS info
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% NB: equations must have one endogenous variable on LHS
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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=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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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, 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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end
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end
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assert(length(lhsidxs) == rows(g1));
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% Initialize AR matrices
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for i = 1:M_.max_endo_lag_orig+1
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oo_.var.(var_model_name).ar{i} = zeros(length(lhsidxs), M_.orig_endo_nbr);
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oo_.var.(var_model_name).artime{i} = 't';
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if i > 1
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oo_.var.(var_model_name).artime{i} = [oo_.var.(var_model_name).artime{i} '-' num2str(i-1)];
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end
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end
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for i = 1:2
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if i == 1
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baselag = 2;
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else
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baselag = 1;
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end
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for j = 1:size(M_.lead_lag_incidence, 2)
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if M_.lead_lag_incidence(i, j) ~= 0 && any(g1(:, M_.lead_lag_incidence(i, j)))
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if j > M_.orig_endo_nbr
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av = M_.aux_vars([M_.aux_vars.endo_index] == j);
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assert(~isempty(av));
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oo_.var.(var_model_name).ar{(av.orig_lead_lag * - 1) + baselag}(:, av.orig_index) = ...
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g1(:, M_.lead_lag_incidence(i, j));
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else
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oo_.var.(var_model_name).ar{baselag}(:, j) = ...
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g1(:, M_.lead_lag_incidence(i, j));
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end
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end
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end
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end
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for i = 1:length(lhsidxs)
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oo_.var.(var_model_name).ar{1}(i, lhsidxs(i)) = oo_.var.(var_model_name).ar{1}(i, lhsidxs(i)) + 1;
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end
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end
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