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function [mu,sigma,offset] = recursive_moments(m0,s0,data,offset)
% Recursive estimation of order one and two moments (expectation and
% covariance matrix).
%
% INPUTS
% o m0 [double] (n*1) vector, the prior expectation.
% o s0 [double] (n*n) matrix, the prior covariance matrix.
% o data [double] (T*n) matrix.
% o offset [integer] scalar, number of observation previously
% used to compute m0 and s0.
% OUTPUTS
% o mu [double] (n*1) vector, the posterior expectation.
% o sigma [double] (n*n) matrix, the posterior covariance matrix.
% o offset [integer] = offset + T.
%
% ALGORITHM
% None.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright © 2006-2017 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
[T,~] = size(data);
for t = 1:T
tt = t+offset;
m1 = m0 + (data(t,:)'-m0)/tt;
qq = m1*m1';
s1 = s0 + ( (data(t,:)'*data(t,:)-qq-s0) + (tt-1)*(m0*m0'-qq') )/tt;
m0 = m1;
s0 = s1;
end
mu = m1;
sigma = s1;
offset = offset+T;
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