67 lines
2.3 KiB
Matlab
67 lines
2.3 KiB
Matlab
function dataset_ = compute_cova(dataset_)
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% Computes the covariance matrix of the sample (possibly with missing observations).
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%@info:
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%! @deftypefn {Function File} {@var{dataset_} =} compute_corr(@var{dataset_})
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%! @anchor{compute_corr}
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%! This function computes covariance matrix of the sample (possibly with missing observations).
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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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%! @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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%! @end table
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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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%! @strong{This function calls:}
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%! @ref{ndim}, @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.cova} is a
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%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
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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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% 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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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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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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dataset_.descriptive.cova = zeros(dataset_.nvobs);
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data = transpose(dataset_.data);
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for i=1:dataset_.info.nvobs
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for j=i:dataset_.info.nvobs
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if dataset_.missing.state
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dataset_.descriptive.cova(i,j) = nanmean(nandemean(data(:,i)).*nandemean(data(:,j)));
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else
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dataset_.descriptive.cova(i,j) = mean(demean(data(:,i)).*demean(data(:,j)));
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end
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if j>i
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dataset_.descriptive.cova(j,i) = dataset_.descriptive.cova(i,j);
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end
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end
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end |