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Cosmetic changes.

time-shift
Stéphane Adjemian (Charybdis) 2018-02-26 12:28:03 +01:00
parent 4a2ac811e1
commit ec003d055f
2 changed files with 19 additions and 19 deletions
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26
matlab/get_ar_matrices.m
View File

@ -1,10 +1,10 @@
function get_ar_matrices(var_model_name)
% Gets the autoregressive matrices associated with the var specified by
% var_model_name. Output stored in cellarray oo_.var.(var_model_name).ar,
% with oo_.var.(var_model_name).ar(i) being the AR matrix at time t-i. Each
% AR matrix is stored with rows organized by the ordering of the equation
% tags found in M_.var.(var_model_name).eqtags and columns organized consistently.
% Gets the autoregressive matrices associated with the var specified by var_model_name.
% Output stored in cellarray oo_.var.(var_model_name).AutoregressiveMatrices, with
% 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 tags
% found in M_.var.(var_model_name).eqtags and columns organized consistently.
%
% INPUTS
%
@ -120,7 +120,7 @@ oo_.var.(var_model_name).ecm_idx = Bvars;
narvars = length(M_.var.(var_model_name).lhs);
nothvars = length(Bvars);
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);
end
@ -140,7 +140,7 @@ for i = 1:2
col = M_.var.(var_model_name).orig_diff_var == av.orig_index;
if any(col)
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));
else
col = Bvars_diff_index == av.orig_index;
@ -151,7 +151,7 @@ for i = 1:2
else
col = M_.var.(var_model_name).lhs == av.orig_index;
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));
else
col = Bvars == av.orig_index;
@ -168,7 +168,7 @@ for i = 1:2
oo_.var.(var_model_name).ecm{baselag}(:, col) = ...
g1(:, M_.lead_lag_incidence(i, j));
else
oo_.var.(var_model_name).ar{baselag}(:, col) = ...
oo_.var.(var_model_name).AutoregressiveMatrices{baselag}(:, col) = ...
g1(:, M_.lead_lag_incidence(i, j));
end
end
@ -177,16 +177,16 @@ for i = 1:2
end
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).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)) = ...
oo_.var.(var_model_name).AutoregressiveMatrices{1}(i, M_.var.(var_model_name).lhs == M_.var.(var_model_name).lhs(i)) + 1;
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.')
end
% 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
if ~ecm_assigned

12
matlab/get_companion_matrix.m
View File

@ -31,20 +31,20 @@ global oo_
get_ar_matrices(var_model_name);
% 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
n = length(oo_.var.(var_model_name).ar{1});
n = length(oo_.var.(var_model_name).AutoregressiveMatrices{1});
% 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
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
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).H((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
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).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
end
end