199 lines
7.4 KiB
Matlab
199 lines
7.4 KiB
Matlab
function get_ar_matrices(var_model_name)
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% Gets the autoregressive matrices associated with the var specified by var_model_name.
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% Output stored in cellarray oo_.var.(var_model_name).AutoregressiveMatrices, with
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% oo_.var.(var_model_name).AutoregressiveMatrices{i} being the AR matrix at time t-i. Each
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% AR matrix is stored with rows organized by the ordering of the equation tags
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% found in M_.var.(var_model_name).eqtags and columns organized consistently.
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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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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_.var.(var_model_name).eqtags, ',')]);
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end
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%% Organize AR matrices
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assert(length(M_.var.(var_model_name).lhs) == rows(g1));
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% Initialize AR matrices
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% ECM
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rhsvars = [];
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rhslag = [];
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maxlag = max(M_.var.(var_model_name).rhs.lag);
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for i = 1:length(M_.var.(var_model_name).rhs.vars_at_eq)
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rhsvars = union(rhsvars, M_.var.(var_model_name).rhs.vars_at_eq{i}.var);
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rhslag = union(rhslag, M_.var.(var_model_name).rhs.vars_at_eq{i}.lag);
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end
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if ~isempty(rhslag)
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maxlag = max(maxlag, max(abs(rhslag)));
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end
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Bvars = setdiff(rhsvars, M_.var.(var_model_name).lhs);
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orig_diff_var_vec = M_.var.(var_model_name).orig_diff_var(M_.var.(var_model_name).diff);
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diff_vars = M_.var.(var_model_name).lhs(M_.var.(var_model_name).lhs > M_.orig_endo_nbr);
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drop_avs_related_to = [];
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for i = 1:length(diff_vars)
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av = M_.aux_vars([M_.aux_vars.endo_index] == diff_vars(i));
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assert(any(orig_diff_var_vec == av.orig_index));
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if av.type == 8
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drop_diff_avs_related_to = [drop_avs_related_to av.orig_index];
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end
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end
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keep = true(length(Bvars), 1);
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Bvars_diff_index = zeros(length(Bvars), 1);
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for i = sum(Bvars <= M_.orig_endo_nbr)+1:length(Bvars)
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av = M_.aux_vars([M_.aux_vars.endo_index] == Bvars(i));
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if av.type == 8
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if any(drop_diff_avs_related_to == av.orig_index)
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assert(any(orig_diff_var_vec == av.orig_index));
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keep(i) = false;
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else
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if any(Bvars_diff_index == av.orig_index)
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keep(i) = false;
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else
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Bvars_diff_index(i) = av.orig_index;
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end
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end
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end
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end
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Bvars = Bvars(keep);
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Bvars_diff_index = Bvars_diff_index(keep);
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oo_.var.(var_model_name).ecm_idx = Bvars;
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% AR
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narvars = length(M_.var.(var_model_name).lhs);
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nothvars = length(Bvars);
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for i = 1:maxlag + 1
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oo_.var.(var_model_name).AutoregressiveMatrices{i} = zeros(narvars, narvars);
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oo_.var.(var_model_name).ecm{i} = zeros(narvars, nothvars);
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end
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ecm_assigned = false;
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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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if av.type == 8
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col = M_.var.(var_model_name).orig_diff_var == av.orig_index;
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if any(col)
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assert(any(orig_diff_var_vec == av.orig_index));
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oo_.var.(var_model_name).AutoregressiveMatrices{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
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g1(:, M_.lead_lag_incidence(i, j));
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else
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col = Bvars_diff_index == av.orig_index;
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ecm_assigned = true;
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oo_.var.(var_model_name).ecm{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
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g1(:, M_.lead_lag_incidence(i, j));
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end
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else
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col = M_.var.(var_model_name).lhs == av.orig_index;
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if any(col)
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oo_.var.(var_model_name).AutoregressiveMatrices{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
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g1(:, M_.lead_lag_incidence(i, j));
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else
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col = Bvars == av.orig_index;
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ecm_assigned = true;
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oo_.var.(var_model_name).ecm{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
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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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else
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col = M_.var.(var_model_name).lhs == j;
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if ~any(col)
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col = Bvars == j;
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ecm_assigned = true;
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oo_.var.(var_model_name).ecm{baselag}(:, col) = ...
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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).AutoregressiveMatrices{baselag}(:, col) = ...
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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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end
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for i = 1:length(M_.var.(var_model_name).lhs)
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oo_.var.(var_model_name).AutoregressiveMatrices{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) = ...
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oo_.var.(var_model_name).AutoregressiveMatrices{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) + 1;
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end
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if any(oo_.var.(var_model_name).AutoregressiveMatrices{1}(:))
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error('This is not a VAR model! Contemporaneous endogenous variables are not allowed.')
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end
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% Remove time t matrix for autoregressive part
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oo_.var.(var_model_name).AutoregressiveMatrices = oo_.var.(var_model_name).AutoregressiveMatrices(2:end);
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if ecm_assigned
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oo_.var.(var_model_name).ecm = oo_.var.(var_model_name).ecm(2:end);
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else
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% Remove error correction matrices if never assigned
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oo_.var.(var_model_name) = rmfield(oo_.var.(var_model_name), 'ecm');
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oo_.var.(var_model_name) = rmfield(oo_.var.(var_model_name), 'ecm_idx');
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end
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end
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