Cosmetic changes.
parent
4a2ac811e1
commit
ec003d055f
|
@ -1,10 +1,10 @@
|
||||||
function get_ar_matrices(var_model_name)
|
function get_ar_matrices(var_model_name)
|
||||||
|
|
||||||
% Gets the autoregressive matrices associated with the var specified by
|
% Gets the autoregressive matrices associated with the var specified by var_model_name.
|
||||||
% var_model_name. Output stored in cellarray oo_.var.(var_model_name).ar,
|
% Output stored in cellarray oo_.var.(var_model_name).AutoregressiveMatrices, with
|
||||||
% with oo_.var.(var_model_name).ar(i) being the AR matrix at time t-i. Each
|
% oo_.var.(var_model_name).AutoregressiveMatrices{i} being the AR matrix at time t-i. Each
|
||||||
% AR matrix is stored with rows organized by the ordering of the equation
|
% AR matrix is stored with rows organized by the ordering of the equation tags
|
||||||
% tags found in M_.var.(var_model_name).eqtags and columns organized consistently.
|
% found in M_.var.(var_model_name).eqtags and columns organized consistently.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
%
|
%
|
||||||
|
@ -120,7 +120,7 @@ oo_.var.(var_model_name).ecm_idx = Bvars;
|
||||||
narvars = length(M_.var.(var_model_name).lhs);
|
narvars = length(M_.var.(var_model_name).lhs);
|
||||||
nothvars = length(Bvars);
|
nothvars = length(Bvars);
|
||||||
for i = 1:maxlag + 1
|
for i = 1:maxlag + 1
|
||||||
oo_.var.(var_model_name).ar{i} = zeros(narvars, narvars);
|
oo_.var.(var_model_name).AutoregressiveMatrices{i} = zeros(narvars, narvars);
|
||||||
oo_.var.(var_model_name).ecm{i} = zeros(narvars, nothvars);
|
oo_.var.(var_model_name).ecm{i} = zeros(narvars, nothvars);
|
||||||
end
|
end
|
||||||
|
|
||||||
|
@ -140,7 +140,7 @@ for i = 1:2
|
||||||
col = M_.var.(var_model_name).orig_diff_var == av.orig_index;
|
col = M_.var.(var_model_name).orig_diff_var == av.orig_index;
|
||||||
if any(col)
|
if any(col)
|
||||||
assert(any(orig_diff_var_vec == av.orig_index));
|
assert(any(orig_diff_var_vec == av.orig_index));
|
||||||
oo_.var.(var_model_name).ar{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
|
oo_.var.(var_model_name).AutoregressiveMatrices{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
|
||||||
g1(:, M_.lead_lag_incidence(i, j));
|
g1(:, M_.lead_lag_incidence(i, j));
|
||||||
else
|
else
|
||||||
col = Bvars_diff_index == av.orig_index;
|
col = Bvars_diff_index == av.orig_index;
|
||||||
|
@ -151,7 +151,7 @@ for i = 1:2
|
||||||
else
|
else
|
||||||
col = M_.var.(var_model_name).lhs == av.orig_index;
|
col = M_.var.(var_model_name).lhs == av.orig_index;
|
||||||
if any(col)
|
if any(col)
|
||||||
oo_.var.(var_model_name).ar{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
|
oo_.var.(var_model_name).AutoregressiveMatrices{(av.orig_lead_lag * - 1) + baselag}(:, col) = ...
|
||||||
g1(:, M_.lead_lag_incidence(i, j));
|
g1(:, M_.lead_lag_incidence(i, j));
|
||||||
else
|
else
|
||||||
col = Bvars == av.orig_index;
|
col = Bvars == av.orig_index;
|
||||||
|
@ -168,7 +168,7 @@ for i = 1:2
|
||||||
oo_.var.(var_model_name).ecm{baselag}(:, col) = ...
|
oo_.var.(var_model_name).ecm{baselag}(:, col) = ...
|
||||||
g1(:, M_.lead_lag_incidence(i, j));
|
g1(:, M_.lead_lag_incidence(i, j));
|
||||||
else
|
else
|
||||||
oo_.var.(var_model_name).ar{baselag}(:, col) = ...
|
oo_.var.(var_model_name).AutoregressiveMatrices{baselag}(:, col) = ...
|
||||||
g1(:, M_.lead_lag_incidence(i, j));
|
g1(:, M_.lead_lag_incidence(i, j));
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
@ -177,16 +177,16 @@ for i = 1:2
|
||||||
end
|
end
|
||||||
|
|
||||||
for i = 1:length(M_.var.(var_model_name).lhs)
|
for i = 1:length(M_.var.(var_model_name).lhs)
|
||||||
oo_.var.(var_model_name).ar{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) = ...
|
oo_.var.(var_model_name).AutoregressiveMatrices{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) = ...
|
||||||
oo_.var.(var_model_name).ar{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) + 1;
|
oo_.var.(var_model_name).AutoregressiveMatrices{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) + 1;
|
||||||
end
|
end
|
||||||
|
|
||||||
if any(oo_.var.(var_model_name).ar{1}(:))
|
if any(oo_.var.(var_model_name).AutoregressiveMatrices{1}(:))
|
||||||
error('This is not a VAR model! Contemporaneous endogenous variables are not allowed.')
|
error('This is not a VAR model! Contemporaneous endogenous variables are not allowed.')
|
||||||
end
|
end
|
||||||
|
|
||||||
% Remove time t matrix for autoregressive part
|
% Remove time t matrix for autoregressive part
|
||||||
oo_.var.(var_model_name).ar = oo_.var.(var_model_name).ar(2:end);
|
oo_.var.(var_model_name).AutoregressiveMatrices = oo_.var.(var_model_name).AutoregressiveMatrices(2:end);
|
||||||
|
|
||||||
% Remove error correction matrices if never assigned
|
% Remove error correction matrices if never assigned
|
||||||
if ~ecm_assigned
|
if ~ecm_assigned
|
||||||
|
|
|
@ -31,20 +31,20 @@ global oo_
|
||||||
get_ar_matrices(var_model_name);
|
get_ar_matrices(var_model_name);
|
||||||
|
|
||||||
% Get the number of lags
|
% Get the number of lags
|
||||||
p = length(oo_.var.(var_model_name).ar);
|
p = length(oo_.var.(var_model_name).AutoregressiveMatrices);
|
||||||
|
|
||||||
% Get the number of variables
|
% Get the number of variables
|
||||||
n = length(oo_.var.(var_model_name).ar{1});
|
n = length(oo_.var.(var_model_name).AutoregressiveMatrices{1});
|
||||||
|
|
||||||
% Initialise the companion matrix
|
% Initialise the companion matrix
|
||||||
oo_.var.(var_model_name).H = zeros(n*p);
|
oo_.var.(var_model_name).CompanionMatrix = zeros(n*p);
|
||||||
|
|
||||||
% Fill the companion matrix
|
% Fill the companion matrix
|
||||||
oo_.var.(var_model_name).H(1:n,1:n) = oo_.var.(var_model_name).ar{1};
|
oo_.var.(var_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(var_model_name).AutoregressiveMatrices{1};
|
||||||
|
|
||||||
if p>1
|
if p>1
|
||||||
for i=2:p
|
for i=2:p
|
||||||
oo_.var.(var_model_name).H(1:n,(i-1)*n+(1:n)) = oo_.var.(var_model_name).ar{i};
|
oo_.var.(var_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(var_model_name).AutoregressiveMatrices{i};
|
||||||
oo_.var.(var_model_name).H((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
|
oo_.var.(var_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
|
||||||
end
|
end
|
||||||
end
|
end
|
Loading…
Reference in New Issue