function output = artf_data(M,N,SigmaE,C,EX,nobs);
%-----------------------------------------------------------------
% PURPOSE: creates artificial data for a specified linear process
%          for purposes of checking the accuracy of estimation
%-----------------------------------------------------------------
% USAGE: output   = artf_data(M,N,SigmaE,C,EX,nobs)
% where: M        = (nx x nx) matrix
%        N        = (nx x ne) matrix
%        SigmaE   = (ne x ne) covariance matrix 
%        C        = (nd x nx) matrix of data
%        EX       = unconditional mean of X
%        nobs     = desired length of time series
%----------------------------------------------------------------
% model setup:
% X(t+1) = M*X(t) + N*e(t+1)    reduced form solution for the 
%                               state vector
% d(t)   = C*X(t)               linear relationship between the 
%                               variables in the data and the 
%                               state vector
% ---------------------------------------------------------------
% RETURNS: an 'output' structure
% output.data = (nobs x nd) matrix of artificial data
% output.nobs = number of observations
%---------------------------------------------------------------
% written by:   Gregory Givens
%               UNC Economics
%               4 October 2003
%---------------------------------------------------------------

nx = size(M,1); ne = size(SigmaE,1);
nd = size(C,1);

t = nobs;
junk = 10000; %throw out 1st 10000 observations%
X = [EX zeros(nx,t)]; D = zeros(nd,t);
E = multirandn(zeros(ne,1),SigmaE,t+junk);
for i = 1:t;
    X(:,i+1) = M*X(:,i) + N*E(:,i+junk);
    D(:,i) = C*X(:,i+1);
end;

output.data = D';
output.nobs = nobs;