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Bug correction and headers. 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1438 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2007-11-08 17:23:53 +00:00
parent a79bd59d91
commit 3ca2614711
2 changed files with 93 additions and 16 deletions
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24
matlab/qr2.m
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@ -1,8 +1,26 @@
function [Q,R] = qr2(X)
% stephane.adjemian@ens.fr [12-07-2005]
% This routine performs a qr decomposition of matrix X such that the
% diagonal scalars of the upper-triangular matrix R are positive. If X
% is a full (column) rank matrix, then R is also the cholesky
% factorization of X'X. This property is needed for the Del Negro
% & Schorfheides's identification scheme.
%
% INPUTS
% See matlab's documentation.
%
% OUTPUTS
% See matlab's documentation.
%
% This routine performs a qr decomposition of matrix X such that the
% diagonal scalars of the upper-triangular matrix R are positive.
% ALGORITHM
% None.
%
% SPECIAL REQUIREMENTS
% None.
%
%
% stephane.adjemian@ens.fr [12-07-2005]
% part of DYNARE, copyright Dynare Team (2007)
% Gnu Public License.
[Q,R] = qr(X);
indx = find(diag(R)<0);
if ~isempty(indx)

85
matlab/var_sample_moments.m
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@ -1,4 +1,61 @@
function [YtY,XtY,YtX,XtX,Y,X] = var_sample_moments(FirstObservation,LastObservation,qlag,var_trend_order);
function [YtY,XtY,YtX,XtX,Y,X] = var_sample_moments(FirstObservation,LastObservation,qlag,var_trend_order)
% Computes the sample moments of a VAR model.
%
% The VAR(p) model is defined by:
%
% y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t for t = 1,...,T
%
% where y_t is a 1*m vector of observed endogenous variables, p is the
% number of lags, A_k is an m*m real matrix, z_t is a 1*q vector of
% exogenous (deterministic) variables, C is a q*m real matrix and
% e_t is a vector of exogenous stochastic shocks. T is the number
% of observations. The deterministic exogenous variables are assumed to
% be a polynomial trend of order q = "var_trend_ordre".
%
% We define:
%
% <> Y = (y_1',y_2',...,y_T')' a T*m matrix,
%
% <> x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
%
% <> X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
%
% <> E = (e_1',e_2',...,e_T')' a T*m matrix and
%
% <> A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
%
% So that we can equivalently write the VAR(p) model using the following
% matrix representation:
%
% Y = X * A +E
%
%
% INPUTS
% o FirstObservation [integer] First observation.
% o LastObservation [integer] Last observation.
% o qlag [integer] Number of lags in the VAR model.
% o var_trend_order [integer] Order of the polynomial exogenous trend:
% = -1 no constant and no linear trend,
% = 0 constant and no linear trend,
% = 1 constant and linear trend.
%
% OUTPUTS
% o YtY [double] Y'*Y an m*m matrix.
% o XtY [double] X'*Y an (mp+q)*m matrix.
% o YtX [double] Y'*X an m*(mp+q) matrix.
% o XtX [double] X'*X an (mp+q)*(mp+q) matrix.
% o Y [double] Y a T*m matrix.
% o X [double] X a T*(mp+q) matrix.
%
% ALGORITHM
% None.
%
% SPECIAL REQUIREMENTS
% None.
%
%
% part of DYNARE, copyright Dynare Team (2007)
% Gnu Public License.
global options_
X = [];
@ -15,7 +72,7 @@ else
end
data = [ ];
for i=1:size(options_.varobs,1)
for i=1:size(options_.varobs,1)% m is equal to options_.varobs
data = [data eval(deblank(options_.varobs(i,:)))];
end
@ -23,20 +80,22 @@ if qlag > FirstObservation
disp('VarSampleMoments :: not enough data to initialize! Try to increase FirstObservation.')
return
end
NumberOfObservations = LastObservation-FirstObservation+1;
NumberOfVariables = size(data,2);
if var_trend_order == -1% No constant
NumberOfObservations = LastObservation-FirstObservation+1;% This is T.
NumberOfVariables = options_.varobs;% This is m.
if var_trend_order == -1% No constant no linear trend case.
X = zeros(NumberOfObservations,NumberOfVariables*qlag);
elseif var_trend_order == 0% Constant
X = zeros(NumberOfObservations,NumberOfVariables*qlag+1);
indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+NumberOfVariables;
elseif var_trend_order == 1;% Constant + Trend
X = zeros(NumberOfObservations,NumberOfVariables*qlag+2);
indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
elseif var_trend_order == 0% Constant and no linear trend case.
X = zeros(NumberOfObservations,NumberOfVariables*qlag+1);
indx = NumberOfVariables*qlag+1;
elseif var_trend_order == 1;% Constant and linear trend case.
X = zeros(NumberOfObservations,NumberOfVariables*qlag+2);
indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
else
disp('var_sample_moments :: trend must be equal to -1,0 or 1!')
return
disp('var_sample_moments :: trend must be equal to -1,0 or 1!')
return
end
% I build matrices Y and X
Y = data(FirstObservation:LastObservation,:);
for t=1:NumberOfObservations