293 lines
9.7 KiB
Matlab
293 lines
9.7 KiB
Matlab
function get_ar_ec_matrices(model_name, model_type)
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%function get_ar_ec_matrices(model_name, model_type)
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%
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% Returns the autoregressive matrix associated with the auxiliary model specified by
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% model_name. Output is stored in cellarray oo_.(model_type).(model_name).ar,
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% with oo_.(model_type).(model_name).ar(:,:,i) being the AR matrix at time t-i. Each
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% AR matrix is stored with rows and columns organized by the ordering of the
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% equation tags found in M_.(model_type).(model_name).eqtags.
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% oo_.(model_type).(model_name).ec contains those entries that are not
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% autoregressive.
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%
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% INPUTS
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%
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% model_name [string] the name of the auxiliary model
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% model_type [string] the type of the auxiliary model ('var' or
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% 'trend_component'. If not passed, the
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% value is set by the function; if a 'var'
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% subfield is found, that is used. Otherwise
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% 'trend_component' is used (if it exists as
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% a subfield of M_.
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%
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% OUTPUTS
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%
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% NONE
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% Copyright © 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 <https://www.gnu.org/licenses/>.
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global M_ oo_
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%% Check inputs
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assert(nargin <= 2, 'This function requires one or two arguments');
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assert(~isempty(model_name) && ischar(model_name), ...
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'The first argument must be a non-empty string');
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if nargin < 2
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model_type = 'var';
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if ~(isfield(M_, model_type) && isfield(M_.(model_type), model_name))
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model_type = 'trend_component';
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if ~(isfield(M_, model_type) && isfield(M_.(model_type), model_name))
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error(['Could not find ' model_name ' in M_.var or ' ...
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'M_.trend_component. First declare it via the var_model ' ...
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'or trend_component_model statement.']);
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end
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end
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else
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assert(~isempty(model_type) && ischar(model_type), ...
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'If provided, the second argument must be a non-empty string');
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if ~(isfield(M_, model_type) && isfield(M_.(model_type), model_name))
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error(['Could not find M_.' model_type '.' model_name ...
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'. First declare it via the var_model or ' ...
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'trend_component_model statement.']);
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end
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end
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%% Call Dynamic Function
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[~, 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_.(model_type).(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_.(model_type).(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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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(any(g1(g1rows, idxs))), ...
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['You cannot have leads in the equations specified by ' strjoin(M_.(model_type).(model_name).eqtags, ',')]);
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end
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%% Organize AR & EC matrices
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assert(length(M_.(model_type).(model_name).lhs) == rows(g1));
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% Find RHS vars for AR & EC matrices
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ecRhsVars = [];
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lhs = M_.(model_type).(model_name).lhs;
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rhsvars = cell(length(lhs), 1);
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for i = 1:length(M_.(model_type).(model_name).rhs.vars_at_eq)
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vars = M_.(model_type).(model_name).rhs.vars_at_eq{i}.var;
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rhsvars{i}.vars = vars;
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rhsvars{i}.lags = M_.(model_type).(model_name).rhs.vars_at_eq{i}.lag;
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rhsvars{i}.arRhsIdxs = [];
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rhsvars{i}.ecRhsIdxs = [];
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rhsvars{i}.ecRhsVars = [];
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for j = 1:length(vars)
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if vars(j) <= M_.orig_endo_nbr
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% vars(j) is not an aux var
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if ismember(vars(j), lhs)
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rhsvars{i}.arRhsIdxs = [rhsvars{i}.arRhsIdxs find(lhs == vars(j))];
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rhsvars{i}.ecRhsIdxs = [rhsvars{i}.ecRhsIdxs -1];
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rhsvars{i}.ecRhsVars = [rhsvars{i}.ecRhsVars -1];
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else
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ecRhsVars = union(ecRhsVars, vars(j));
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rhsvars{i}.ecRhsVars = [rhsvars{i}.ecRhsVars vars(j)];
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rhsvars{i}.arRhsIdxs = [rhsvars{i}.arRhsIdxs -1];
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rhsvars{i}.ecRhsIdxs = [rhsvars{i}.ecRhsIdxs find(rhsvars{i}.ecRhsVars == vars(j))];
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end
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else
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% Search aux vars for matching lhs var
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lhsvaridx = findLhsInAuxVar(vars(j), lhs);
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if lhsvaridx >= 1
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rhsvars{i}.arRhsIdxs = [rhsvars{i}.arRhsIdxs find(lhs == lhsvaridx)];
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rhsvars{i}.ecRhsIdxs = [rhsvars{i}.ecRhsIdxs -1];
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rhsvars{i}.ecRhsVars = [rhsvars{i}.ecRhsVars -1];
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else
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% otherwise find endog that corresponds to this aux var
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varidx = findVarNoLag(vars(j));
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ecRhsVars = union(ecRhsVars, varidx);
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rhsvars{i}.ecRhsVars = [rhsvars{i}.ecRhsVars varidx];
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rhsvars{i}.arRhsIdxs = [rhsvars{i}.arRhsIdxs -1];
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rhsvars{i}.ecRhsIdxs = [rhsvars{i}.ecRhsIdxs find(rhsvars{i}.ecRhsVars == varidx)];
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end
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end
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end
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end
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[rhsvars, ecRhsVars] = reorderECvars(rhsvars, ecRhsVars, lhs);
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% Initialize matrices
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oo_.(model_type).(model_name).ar = zeros(length(lhs), length(lhs), max(M_.(model_type).(model_name).max_lag));
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oo_.(model_type).(model_name).ec = zeros(length(lhs), length(ecRhsVars), 1);
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oo_.(model_type).(model_name).ar_idx = lhs;
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oo_.(model_type).(model_name).ec_idx = ecRhsVars;
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% Fill matrices
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for i = 1:length(lhs)
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for j = 1:length(rhsvars{i}.vars)
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var = rhsvars{i}.vars(j);
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if rhsvars{i}.lags(j) == -1
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g1col = M_.lead_lag_incidence(1, var);
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else
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g1col = M_.lead_lag_incidence(2, var);
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end
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if g1col ~= 0 && any(g1(:, g1col))
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if rhsvars{i}.arRhsIdxs(j) > 0
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% Fill AR
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lag = findLagForVar(var, -rhsvars{i}.lags(j), 0, lhs);
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oo_.(model_type).(model_name).ar(i, rhsvars{i}.arRhsIdxs(j), lag) = ...
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oo_.(model_type).(model_name).ar(i, rhsvars{i}.arRhsIdxs(j), lag) + g1(i, g1col);
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elseif rhsvars{i}.ecRhsIdxs(j) > 0
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% Fill EC
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lag = findLagForVar(var, -rhsvars{i}.lags(j), 0, ecRhsVars);
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if lag==1
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if size(oo_.(model_type).(model_name).ec, 3) < lag
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oo_.(model_type).(model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) = 0;
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end
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oo_.(model_type).(model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) = ...
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oo_.(model_type).(model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) + g1(i, g1col);
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end
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else
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error('Shouldn''t arrive here');
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end
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end
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end
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end
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end
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function [rhsvars, ecRhsVarsReordered] = reorderECvars(rhsvars, ecRhsVars, lhs)
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global M_
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ecRhsVarsReordered = [];
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for i = 1:length(lhs)
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av = M_.aux_vars([M_.aux_vars.endo_index] == lhs(i));
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if ~isempty(av)
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var = ecRhsVars(ecRhsVars == av.orig_index);
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if ~isempty(var)
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ecRhsVarsReordered = [ecRhsVarsReordered var];
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for j = 1:length(rhsvars)
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rhsidx = find(rhsvars{j}.ecRhsVars == var);
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if ~isempty(rhsidx)
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rhsvars{j}.ecRhsIdxs(rhsidx) = length(ecRhsVarsReordered);
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end
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end
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end
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end
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end
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end
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function lhsvaridx = findLhsInAuxVar(auxVar, lhsvars)
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global M_
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if auxVar <= M_.orig_endo_nbr
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lhsvaridx = -1;
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return
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end
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av = M_.aux_vars([M_.aux_vars.endo_index] == auxVar);
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if av.type == 8 || av.type == 10
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if ismember(av.endo_index, lhsvars)
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lhsvaridx = av.endo_index;
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else
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lhsvaridx = findLhsInAuxVar(av.orig_index, lhsvars);
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end
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else
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if ismember(av.orig_index, lhsvars)
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lhsvaridx = av.orig_index;
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else
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lhsvaridx = findLhsInAuxVar(av.orig_index, lhsvars);
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end
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end
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end
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function idx = findVarNoLag(auxVar)
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global M_
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if auxVar <= M_.orig_endo_nbr
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error('Shouldn''t arrive here')
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end
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av = M_.aux_vars([M_.aux_vars.endo_index] == auxVar);
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if av.type == 8 || av.type == 10
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idx = av.endo_index;
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else
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if av.orig_index <= M_.orig_endo_nbr
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idx = av.orig_index;
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else
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idx = findVarNoLag(av.orig_index);
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end
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end
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end
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function [lag, ndiffs] = findLagForVar(auxVar, lag, ndiffs, rhsVars)
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global M_
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if auxVar <= M_.orig_endo_nbr
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assert(lag > 0)
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return
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end
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av = M_.aux_vars([M_.aux_vars.endo_index] == auxVar);
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if av.type == 8
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ndiffs = ndiffs + 1;
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end
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if ismember(av.endo_index, rhsVars)
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if av.type == 8 || av.type == 9
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lag = lag + abs(av.orig_lead_lag);
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end
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elseif ismember(av.orig_index, rhsVars)
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if av.orig_index <= M_.orig_endo_nbr
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lag = lag + abs(av.orig_lead_lag);
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else
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[lag, ndiffs] = findLagForVar(av.orig_index, lag + max(1, abs(av.orig_lead_lag)), ndiffs, rhsVars);
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end
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else
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if av.type == 8
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[lag, ndiffs] = findLagForVar(av.orig_index, lag, ndiffs, rhsVars);
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else
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[lag, ndiffs] = findLagForVar(av.orig_index, lag + max(1, abs(av.orig_lead_lag)), ndiffs, rhsVars);
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end
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end
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assert(lag > 0)
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end
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