get_ar_ec_matrices: changes for introduction of trend_component_model statement
Houtan Bastani
parent
7c541b3bba
commit
b27267da9b
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@ -1,17 +1,23 @@
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function get_ar_ec_matrices(var_model_name)
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%function get_ar_ec_matrices(var_model_name)
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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 VAR specified by
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% var_model_name. Output is stored in cellarray oo_.var.(var_model_name).ar,
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% with oo_.var.(var_model_name).ar(:,:,i) being the AR matrix at time t-i. Each
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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_.var.(var_model_name).eqtags.
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% oo_.var.(var_model_name).ec contains those entries that are not
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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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% var_model_name [string] the name of the VAR model
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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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@ -36,13 +42,29 @@ function get_ar_ec_matrices(var_model_name)
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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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%% 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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@ -54,10 +76,10 @@ end
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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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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_.var.(var_model_name).eqtags{i});
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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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@ -68,20 +90,20 @@ g1 = -1 * g1(g1rows, :);
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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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['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_.var.(var_model_name).lhs) == rows(g1));
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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_.var.(var_model_name).lhs;
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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_.var.(var_model_name).rhs.vars_at_eq)
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vars = M_.var.(var_model_name).rhs.vars_at_eq{i}.var;
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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_.var.(var_model_name).rhs.vars_at_eq{i}.lag;
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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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for j = 1:length(vars)
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@ -113,10 +135,10 @@ for i = 1:length(M_.var.(var_model_name).rhs.vars_at_eq)
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end
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% Initialize matrices
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oo_.var.(var_model_name).ar = zeros(length(lhs), length(lhs), M_.var.(var_model_name).max_lag);
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oo_.var.(var_model_name).ec = zeros(length(lhs), length(ecRhsVars), M_.var.(var_model_name).max_lag);
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oo_.var.(var_model_name).ar_idx = lhs;
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oo_.var.(var_model_name).ec_idx = ecRhsVars;
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oo_.(model_type).(model_name).ar = zeros(length(lhs), length(lhs), M_.(model_type).(model_name).max_lag);
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oo_.(model_type).(model_name).ec = zeros(length(lhs), length(ecRhsVars), M_.(model_type).(model_name).max_lag);
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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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@ -131,16 +153,16 @@ for i = 1:length(lhs)
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if rhsvars{i}.arRhsIdxs(j) > 0
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% Fill AR
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[lag, ndiffs] = findLagForVar(var, -rhsvars{i}.lags(j), 0, lhs);
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oo_.var.(var_model_name).ar(i, rhsvars{i}.arRhsIdxs(j), lag) = ...
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oo_.var.(var_model_name).ar(i, rhsvars{i}.arRhsIdxs(j), lag) + g1(i, g1col);
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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, ndiffs] = findLagForVar(var, -rhsvars{i}.lags(j), 0, ecRhsVars);
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if size(oo_.var.(var_model_name).ec, 3) < lag
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oo_.var.(var_model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) = 0;
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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_.var.(var_model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) = ...
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oo_.var.(var_model_name).ec(i, rhsvars{i}.ecRhsIdxs(j), lag) + g1(i, g1col);
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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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else
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error('Shouldn''t arrive here');
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end
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