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Rewrote compute_acov and renamed it as nanautocovariance.

time-shift
Stéphane Adjemian (Charybdis) 2014-05-23 15:07:46 +02:00
parent dd223e41f5
commit bdd7b8aacc
1 changed files with 32 additions and 31 deletions
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63
matlab/utilities/dataset/compute_acov.m → matlab/utilities/dataset/nanautocovariance.m
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@ -1,4 +1,4 @@
function dataset_ = compute_acov(dataset_) function autocov = nanautocovariance(data,order)
% Computes the (multivariate) auto-covariance function of the sample (possibly with missing observations). % Computes the (multivariate) auto-covariance function of the sample (possibly with missing observations).
%@info: %@info:
@ -8,36 +8,36 @@ function dataset_ = compute_acov(dataset_)
%! %!
%! @strong{Inputs} %! @strong{Inputs}
%! @table @var %! @table @var
%! @item dataset_ %! @item data
%! Dynare structure describing the dataset, built by @ref{initialize_dataset} %! T*N array of real numbers.
%! @item nlag %! @item order
%! Integer scalar. The maximum number of lags of the autocovariance function. %! Integer scalar. The maximum number of lags of the autocovariance function.
%! @end table %! @end table
%! %!
%! @strong{Outputs} %! @strong{Outputs}
%! @table @var %! @table @var
%! @item dataset_ %! @item autocov
%! Dynare structure describing the dataset, built by @ref{initialize_dataset} %! A N*N*order array of real numbers.
%! @end table %! @end table
%! %!
%! @strong{This function is called by:} %! @strong{This function is called by:}
%! @ref{descriptive_statistics}. %! @ref{descriptive_statistics}.
%! %!
%! @strong{This function calls:} %! @strong{This function calls:}
%! @ref{ndim}, @ref{compute_cova}, @ref{demean}, @ref{nandemean}. %! @ref{ndim}, @ref{nancovariance}, @ref{demean}, @ref{nandemean}.
%! %!
%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a %! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a
%! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}, %! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs},
%! and @tex{n} is the maximum number of lags given by the second input @code{nlag}). %! and @tex{n} is the maximum number of lags given by the second input @code{nlag}).
%! %!
%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the %! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the
%! computation of the auto-covariance function. %! computation of the auto-covariance function.
%! %!
%! @end deftypefn %! @end deftypefn
%@eod: %@eod:
% Copyright (C) 2011-2012 Dynare Team % Copyright (C) 2011-2014 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
% Dynare is free software: you can redistribute it and/or modify % Dynare is free software: you can redistribute it and/or modify
@ -53,22 +53,23 @@ function dataset_ = compute_acov(dataset_)
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr n = size(data,2);
missing = isanynan(data);
if ~isfield(dataset_.descriptive,'cova') autocov = zeros(n, n, order);
dataset_ = compute_cova(dataset_);
end
dataset_.descriptive.acov = zeros(dataset_.nvobs,dataset_.nvobs,nlag);
data = transpose(dataset_.data); for lag=1:order
if missing
for lag=1:nlag data = nandemean(data);
for i=1:dataset_.info.nvobs else
for j=1:dataset_.info.nvobs data = demean(data);
if dataset_.missing.state end
dataset_.descriptive.acov(i,j,lag) = nanmean(nandemean(data(lag+1:end,i)).*nandemean(data(1:end-lag,j))); for i=1:n
for j=1:n
if missing
autocov(i,j,lag) = nanmean(data((lag+1):end,i).*data(1:end-lag,j));
else else
dataset_.descriptive.acov(i,j,lag) = mean(demean(data(lag+1:end,i)).*demean(data(1:end-lag,j))); autocov(i,j,lag) = mean(data((lag+1):end,i).*data(1:end-lag,j));
end end
end end
end end