%
% prodmom.m		Date: 4/29/2006
% This MATLAB program computes the product moment of X_{i_1}^{nu_1}X_{i_2}^{nu_2}...X_{i_m}^{nu_m},
% where X_{i_j} are elements from X ~ N(0_n,V).
% V only needs to be positive semidefinite.
% V: variance-covariance matrix of X
% ii: vector of i_j
% nu: power of X_{i_j}
% Reference: Triantafyllopoulos (2003) On the Central Moments of the Multidimensional
%            Gaussian Distribution, Mathematical Scientist
%            Kotz, Balakrishnan, and Johnson (2000), Continuous Multivariate
%            Distributions, Vol. 1, p.261
% Note that there is a typo in Eq.(46.25), there should be an extra rho in front
% of the equation.
% Usage: prodmom(V,[i1 i2 ... ir],[nu1 nu2 ... nur])
% Example: To get E[X_2X_4^3X_7^2], use prodmom(V,[2 4 7],[1 3 2])
%
% Retrieved from http://www-2.rotman.utoronto.ca/~kan/papers/prodmom.zip
% This function is part of replication codes of the following paper:
% Kan, R.: "From moments of sum to moments of product." Journal of
% Multivariate Analysis, 2008, vol. 99, issue 3, pages 542-554.

% Copyright © 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
% Copyright © 2019-2025 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/>.

function y = prodmom(V,ii,nu)
if nargin<3
   nu = ones(size(ii));
end
s = sum(nu);
if s==0
   y = 1;
   return
end
if rem(s,2)==1
   y = 0;
   return
end
nuz = nu==0;
nu(nuz) = [];
ii(nuz) = [];
m = length(ii);
V = V(ii,ii);
s2 = s/2;
%
%  Use univariate normal results
%
if m==1
   y = V^s2*prod(1:2:s-1);
   return
end
%
%  Use bivariate normal results when there are only two distinct indices
%
if m==2
    if V(1,1)==0 || V(2,2)==0
        y=0;
        return
    end
   rho = V(1,2)/sqrt(V(1,1)*V(2,2));
   y = V(1,1)^(nu(1)/2)*V(2,2)^(nu(2)/2)*pruned_SS.bivmom(nu,rho);
   return
end
%
%  Regular case
%
[nu,inu] = sort(nu,2,'descend');
V = V(inu,inu);          % Extract only the relevant part of V
x = zeros(1,m);
V = V./2;
nu2 = nu./2;
p = 2;
q = nu2*V*nu2';
y = 0;
for i=1:fix(prod(nu+1)/2)
    y = y+p*q^s2;
    for j=1:m
        if x(j)<nu(j)
           x(j) = x(j)+1;
           p = -round(p*(nu(j)+1-x(j))/x(j));
           q = q-2*(nu2-x)*V(:,j)-V(j,j);
           break
        else
           x(j) = 0;
           if rem(nu(j),2)==1
              p = -p;
           end
           q = q+2*nu(j)*(nu2-x)*V(:,j)-nu(j)^2*V(j,j);
        end
    end
end
y = y/prod(1:s2);
