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