diff --git a/matlab/autoregressive_process_specification.m b/matlab/autoregressive_process_specification.m
new file mode 100644
index 0000000000000000000000000000000000000000..d802439e0bc1e44c9aca430e07847f8f4c08c948
--- /dev/null
+++ b/matlab/autoregressive_process_specification.m
@@ -0,0 +1,97 @@
+function [InnovationVariance,AutoregressiveParameters] = autoregressive_process_specification(Variance,Rho,p)
+% This function computes the parameters of an AR(p) process from the variance and the autocorrelation function
+% (the first p terms) of this process.
+%
+% INPUTS 
+%  [1] Variance                 [double]  scalar, variance of the variable.
+%  [2] Rho                      [double]  p*1 vector, the autocorelation function: \rho(1), \rho(2), ..., \rho(p).
+%  [3] p                        [double]  scalar, the number of lags in the AR process.
+%
+% OUTPUTS 
+%  [1] InnovationVariance       [double]  scalar, the variance of the innovation.
+%  [2] AutoregressiveParameters [double]  p*1 vector of autoregressive parameters.
+%
+% NOTES 
+%
+% The AR(p) model for {y_t} is:
+%   
+%           y_t = \phi_1 * y_{t-1} +  \phi_2 * y_{t-2} + ... +  \phi_p * y_{t-p} + e_t    
+%
+% Let \gamma(0) and \rho(1), ..., \rho(2) be the variance and the autocorrelation function of {y_t}. This function
+% compute the variance of {e_t} and the \phi_i (i=1,...,p) from the variance and the autocorrelation function of {y_t}. 
+% We know that:
+%    
+%    \gamma(0) = \phi_1 \gamma(1) + ... + \phi_p \gamma(p) + \sigma^2
+%
+% where \sigma^2 is the variance of {e_t}. Equivalently we have:
+%
+%    \sigma^2 = \gamma(0) (1-\rho(1)\phi_1 - ... - \rho(p)\phi_p)     
+%
+% We also have for any integer  h>0:
+% 
+%    \rho(h) = \phi_1 \rho(h-1) + ... + \phi_p \rho(h-p)
+%
+% We can write the equations for \rho(1), ..., \rho(p) using matrices. Let R be the p*p autocorelation
+% matrix and v be the p*1 vector gathering the first p terms of the autocorrelation function. We have: 
+%
+%           v = R*PHI
+%    
+% where PHI is a p*1 vector with the autoregressive parameters of the AR(p) process. We can recover the autoregressive
+% parameters by inverting the autocorrelation matrix: PHI = inv(R)*v.
+% 
+% This function first computes the vector PHI by inverting R and computes the variance of the innovation by evaluating
+%
+%           \sigma^2 = \gamma(0)*(1-PHI'*v)
+
+% Copyright (C) 2009 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 <http://www.gnu.org/licenses/>.
+    AutoregressiveParameters = NaN(p,1);
+    InnovationVariance = NaN;
+    switch p
+      case 1
+        AutoregressiveParameters = Rho(1);
+      case 2
+        tmp = (Rho(2)-1)/(Rho(1)*Rho(1)-1);
+        AutoregressiveParameters(1) = Rho(1)*tmp;
+        AutoregressiveParameters(2) = 1-tmp;
+      case 3
+        t1 = 1/(Rho(2)-2*Rho(1)*Rho(1)+1);
+        t2 = (1.5*Rho(1)-2*Rho(1)*Rho(1)*Rho(1)+.5*Rho(3))*t1;
+        t3 = .5*(Rho(1)- Rho(3))/(Rho(2)-1);
+        AutoregressiveParameters(1) = t2-t3-Rho(1);
+        AutoregressiveParameters(2) = (Rho(2)*Rho(2)-Rho(3)*Rho(1)-Rho(1)*Rho(1)+Rho(2))*t1 ;
+        AutoregressiveParameters(3) = t3-Rho(1)+t2;
+      case 4
+        AutoregressiveParameters(1) = (Rho(1)*Rho(1)*Rho(1)*(Rho(4)+2)-Rho(1)*(Rho(3)*Rho(3)+1)+Rho(3)*Rho(4)-Rho(2)*Rho(2)*(Rho(3)-Rho(1)*Rho(4))-Rho(1)*Rho(1)*(Rho(3)+Rho(3)*Rho(4))+Rho(2)*(2*Rho(3)*Rho(1)*Rho(1)-3*Rho(1)*Rho(1)*Rho(1)+(Rho(3)*Rho(3)-2*Rho(4)+1)*Rho(1)+Rho(3))+Rho(2)*Rho(2)*Rho(2)*(Rho(1)-Rho(3)))/(2*Rho(1)*Rho(1)*Rho(1)*Rho(3)-Rho(1)*Rho(1)*Rho(1)*Rho(1)+2*Rho(1)*Rho(1)*Rho(2)*Rho(2)-4*Rho(1)*Rho(1)*Rho(2)-Rho(1)*Rho(1)*Rho(3)*Rho(3)+3*Rho(1)*Rho(1)+2*Rho(1)*Rho(2)*Rho(2)*Rho(3)-4*Rho(1)*Rho(2)*Rho(3)-Rho(2)*Rho(2)*Rho(2)*Rho(2)+2*Rho(2)*Rho(2)+Rho(3)*Rho(3)-1);
+        AutoregressiveParameters(2) = ((Rho(2)*Rho(2)-Rho(3)*Rho(1)-Rho(1)*Rho(1)+Rho(2))*(Rho(1)*Rho(1)-2*Rho(1)*Rho(3)+Rho(3)*Rho(3)+Rho(2)+Rho(4)-Rho(2)*Rho(4)-1))/(2*Rho(1)*Rho(1)*Rho(1)*Rho(3)-Rho(1)*Rho(1)*Rho(1)*Rho(1)+2*Rho(1)*Rho(1)*Rho(2)*Rho(2)-4*Rho(1)*Rho(1)*Rho(2)-Rho(1)*Rho(1)*Rho(3)*Rho(3)+3*Rho(1)*Rho(1)+2*Rho(1)*Rho(2)*Rho(2)*Rho(3)-4*Rho(1)*Rho(2)*Rho(3)-Rho(2)*Rho(2)*Rho(2)*Rho(2)+2*Rho(2)*Rho(2)+Rho(3)*Rho(3)-1);
+        AutoregressiveParameters(3) = (Rho(1)*Rho(1)*(2*Rho(2)*Rho(3)+Rho(3)*Rho(4))-Rho(3)+Rho(2)*Rho(2)*Rho(3)+Rho(2)*Rho(2)*Rho(2)*Rho(3)+Rho(1)*((Rho(4)-2)*Rho(2)*Rho(2)-Rho(2)*Rho(2)*Rho(2)+(2-Rho(4)-3*Rho(3)*Rho(3))*Rho(2)+Rho(4))+Rho(3)*Rho(3)*Rho(3)-Rho(1)*Rho(1)*Rho(1)*(Rho(4)-Rho(2)+1)-Rho(2)*Rho(3)*Rho(4))/(2*Rho(1)*Rho(1)*Rho(1)*Rho(3)-Rho(1)*Rho(1)*Rho(1)*Rho(1)+2*Rho(1)*Rho(1)*Rho(2)*Rho(2)-4*Rho(1)*Rho(1)*Rho(2)-Rho(1)*Rho(1)*Rho(3)*Rho(3)+3*Rho(1)*Rho(1)+2*Rho(1)*Rho(2)*Rho(2)*Rho(3)-4*Rho(1)*Rho(2)*Rho(3)-Rho(2)*Rho(2)*Rho(2)*Rho(2)+2*Rho(2)*Rho(2)+Rho(3)*Rho(3)-1);
+        AutoregressiveParameters(4) = (Rho(1)+Rho(3)/2-Rho(4)/2-(3*Rho(1)*Rho(2))/2+(Rho(1)*Rho(4))/2-(Rho(2)*Rho(3))/2+Rho(2)*Rho(2)-1/2)/(Rho(3)-Rho(1)*(2*Rho(2)+Rho(3)-1)+Rho(1)*Rho(1)+Rho(2)*Rho(2)-1)+(Rho(1)+Rho(3)/2+Rho(4)/2-(3*Rho(1)*Rho(2))/2+(Rho(1)*Rho(4))/2-(Rho(2)*Rho(3))/2-Rho(2)*Rho(2)+1/2)/(Rho(3)+Rho(1)*(Rho(3)-2*Rho(2)+1)-Rho(1)*Rho(1)-Rho(2)*Rho(2)+1)-1;
+      case 5
+           AutoregressiveParameters(1) = (Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(1) - 3*Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(3) + Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(1) - 5*Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(4) + 6*Rho(1)*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(1)*Rho(3)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(3) - Rho(1)*Rho(1)*Rho(1)*Rho(4)*Rho(4) - Rho(1)*Rho(1)*Rho(1)*Rho(4) - 3*Rho(1)*Rho(1)*Rho(1) + Rho(1)*Rho(1)*Rho(2)*Rho(2)*Rho(3) + 2*Rho(5)*Rho(1)*Rho(1)*Rho(2)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(2)*Rho(3)*Rho(4) + 3*Rho(1)*Rho(1)*Rho(2)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(1)*Rho(2)*Rho(4) - 3*Rho(5)*Rho(1)*Rho(1)*Rho(2) + Rho(1)*Rho(1)*Rho(3)*Rho(3)*Rho(3) + Rho(5)*Rho(1)*Rho(1)*Rho(3)*Rho(3) + Rho(1)*Rho(1)*Rho(3)*Rho(4)*Rho(4) - Rho(1)*Rho(1)*Rho(3)*Rho(4) + Rho(1)*Rho(1)*Rho(3) + 2*Rho(5)*Rho(1)*Rho(1)*Rho(4) + 2*Rho(1)*Rho(2)*Rho(2)*Rho(2)*Rho(2) - Rho(1)*Rho(2)*Rho(2)*Rho(3)*Rho(3) + 2*Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(3) + Rho(1)*Rho(2)*Rho(2)*Rho(4)*Rho(4) - 4*Rho(1)*Rho(2)*Rho(2)*Rho(4) - Rho(1)*Rho(2)*Rho(2) - 2*Rho(1)*Rho(2)*Rho(3)*Rho(3)*Rho(4) - 4*Rho(1)*Rho(2)*Rho(3)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(2)*Rho(3) - Rho(1)*Rho(2)*Rho(4)*Rho(4) + 2*Rho(1)*Rho(2)*Rho(4) - Rho(1)*Rho(2) - Rho(1)*Rho(3)*Rho(3)*Rho(3)*Rho(3) - Rho(1)*Rho(3)*Rho(3)*Rho(4) + 2*Rho(5)*Rho(1)*Rho(3) + Rho(1)*Rho(4)*Rho(4) + Rho(1) - Rho(2)*Rho(2)*Rho(2)*Rho(2)*Rho(3) - Rho(5)*Rho(2)*Rho(2)*Rho(2)*Rho(2) + Rho(2)*Rho(2)*Rho(3)*Rho(3)*Rho(3) + Rho(2)*Rho(2)*Rho(3)*Rho(4) + Rho(2)*Rho(2)*Rho(3) + Rho(5)*Rho(2)*Rho(2)*Rho(4) + Rho(5)*Rho(2)*Rho(2) + Rho(2)*Rho(3)*Rho(3)*Rho(3) - Rho(5)*Rho(2)*Rho(3)*Rho(3) - Rho(2)*Rho(3)*Rho(4)*Rho(4) + 2*Rho(2)*Rho(3)*Rho(4) - Rho(2)*Rho(3) + Rho(3)*Rho(3)*Rho(3)*Rho(4) - Rho(3)*Rho(4) - Rho(5)*Rho(4))/((Rho(1)*Rho(1) - 2*Rho(1)*Rho(3) + Rho(3)*Rho(3) + Rho(2) + Rho(4) - Rho(2)*Rho(4) - 1)*(2*Rho(1)*Rho(3) - Rho(4) - Rho(2) - Rho(2)*Rho(4) - 4*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(4) + 3*Rho(1)*Rho(1) + 2*Rho(2)*Rho(2) + 2*Rho(2)*Rho(2)*Rho(2) + Rho(3)*Rho(3) - 4*Rho(1)*Rho(2)*Rho(3) - 1));
+           AutoregressiveParameters(2) =  (3*Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(2) - Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(4) - 2*Rho(1)*Rho(1)*Rho(1)*Rho(1) - Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(3) - Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(2) + Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(4) + Rho(5)*Rho(1)*Rho(1)*Rho(1) - 2*Rho(1)*Rho(1)*Rho(2)*Rho(2)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(2)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(2)*Rho(3)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(1)*Rho(2)*Rho(3) - 3*Rho(1)*Rho(1)*Rho(2)*Rho(4)*Rho(4) + 4*Rho(1)*Rho(1)*Rho(2)*Rho(4) + Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(3)*Rho(3)*Rho(4) + 3*Rho(1)*Rho(1)*Rho(3)*Rho(3) - Rho(5)*Rho(1)*Rho(1)*Rho(3)*Rho(4) + Rho(1)*Rho(1)*Rho(4)*Rho(4)*Rho(4) + Rho(1)*Rho(1)*Rho(4)*Rho(4) - Rho(1)*Rho(1)*Rho(4) + Rho(1)*Rho(1) + 3*Rho(1)*Rho(2)*Rho(2)*Rho(2)*Rho(3) + Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(2) + 3*Rho(1)*Rho(2)*Rho(2)*Rho(3)*Rho(4) - Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(4) + 2*Rho(5)*Rho(1)*Rho(2)*Rho(2) - Rho(1)*Rho(2)*Rho(3)*Rho(3)*Rho(3) + 3*Rho(5)*Rho(1)*Rho(2)*Rho(3)*Rho(3) - Rho(1)*Rho(2)*Rho(3)*Rho(4)*Rho(4) + Rho(1)*Rho(2)*Rho(3)*Rho(4) - 3*Rho(1)*Rho(2)*Rho(3) + Rho(5)*Rho(1)*Rho(2)*Rho(4) - 2*Rho(5)*Rho(1)*Rho(2) - Rho(1)*Rho(3)*Rho(3)*Rho(3)*Rho(4) - Rho(1)*Rho(3)*Rho(3)*Rho(3) - Rho(1)*Rho(3)*Rho(4)*Rho(4) - 3*Rho(1)*Rho(3)*Rho(4) + Rho(1)*Rho(3) - Rho(5)*Rho(1)*Rho(4) - Rho(2)*Rho(2)*Rho(2)*Rho(3)*Rho(3) - Rho(5)*Rho(2)*Rho(2)*Rho(2)*Rho(3) + 2*Rho(2)*Rho(2)*Rho(2)*Rho(4)*Rho(4) - 4*Rho(2)*Rho(2)*Rho(2)*Rho(4) + 2*Rho(2)*Rho(2)*Rho(2) - 2*Rho(2)*Rho(2)*Rho(3)*Rho(3)*Rho(4) + Rho(2)*Rho(2)*Rho(3)*Rho(3) - Rho(5)*Rho(2)*Rho(2)*Rho(3) + Rho(2)*Rho(3)*Rho(3)*Rho(3)*Rho(3) + Rho(2)*Rho(3)*Rho(3)*Rho(4) + Rho(5)*Rho(2)*Rho(3)*Rho(4) - Rho(2)*Rho(4)*Rho(4)*Rho(4) + Rho(2)*Rho(4)*Rho(4) + Rho(2)*Rho(4) - Rho(2) - Rho(5)*Rho(3)*Rho(3)*Rho(3) + Rho(3)*Rho(3)*Rho(4)*Rho(4) + Rho(5)*Rho(3))/((Rho(2) + Rho(4) - 2*Rho(1)*Rho(3) + Rho(2)*Rho(4) + 4*Rho(1)*Rho(1)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(4) - 3*Rho(1)*Rho(1) - 2*Rho(2)*Rho(2) - 2*Rho(2)*Rho(2)*Rho(2) - Rho(3)*Rho(3) + 4*Rho(1)*Rho(2)*Rho(3) + 1)*(Rho(1)*Rho(1) - 2*Rho(1)*Rho(3) + Rho(3)*Rho(3) + Rho(2) + Rho(4) - Rho(2)*Rho(4) - 1));
+          AutoregressiveParameters(3) = (Rho(1)*Rho(1)*Rho(1) + Rho(5)*Rho(1)*Rho(1) - 2*Rho(1)*Rho(2) - 2*Rho(1)*Rho(3)*Rho(3) + Rho(5)*Rho(1)*Rho(3) - Rho(1)*Rho(4)*Rho(4) - Rho(1)*Rho(4) + Rho(2)*Rho(2)*Rho(3) - Rho(5)*Rho(2)*Rho(2) + 2*Rho(2)*Rho(3)*Rho(4) + Rho(2)*Rho(3) - Rho(5)*Rho(2) - Rho(3)*Rho(3)*Rho(3) + Rho(3)*Rho(4) + Rho(3))/(Rho(2) + Rho(4) - 2*Rho(1)*Rho(3) + Rho(2)*Rho(4) + 4*Rho(1)*Rho(1)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(4) - 3*Rho(1)*Rho(1) - 2*Rho(2)*Rho(2) - 2*Rho(2)*Rho(2)*Rho(2) - Rho(3)*Rho(3) + 4*Rho(1)*Rho(2)*Rho(3) + 1);
+          AutoregressiveParameters(4) = (Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(2) - Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(4) - Rho(1)*Rho(1)*Rho(1)*Rho(1) + Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(3) - 3*Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(1)*Rho(3)*Rho(4) + Rho(1)*Rho(1)*Rho(1)*Rho(3) + Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(4) + 2*Rho(5)*Rho(1)*Rho(1)*Rho(1) + 4*Rho(1)*Rho(1)*Rho(2)*Rho(2)*Rho(4) - 4*Rho(1)*Rho(1)*Rho(2)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(2)*Rho(3)*Rho(3) + 2*Rho(5)*Rho(1)*Rho(1)*Rho(2)*Rho(3) - 3*Rho(1)*Rho(1)*Rho(2)*Rho(4)*Rho(4) + 3*Rho(1)*Rho(1)*Rho(2) - 2*Rho(1)*Rho(1)*Rho(3)*Rho(3)*Rho(4) - 2*Rho(1)*Rho(1)*Rho(3)*Rho(3) - Rho(5)*Rho(1)*Rho(1)*Rho(3)*Rho(4) - Rho(5)*Rho(1)*Rho(1)*Rho(3) + Rho(1)*Rho(1)*Rho(4)*Rho(4)*Rho(4) - Rho(1)*Rho(1)*Rho(4) - Rho(1)*Rho(2)*Rho(2)*Rho(2)*Rho(3) + Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(2) + Rho(1)*Rho(2)*Rho(2)*Rho(3)*Rho(4) - 2*Rho(1)*Rho(2)*Rho(2)*Rho(3) + Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(4) + Rho(1)*Rho(2)*Rho(3)*Rho(3)*Rho(3) + Rho(5)*Rho(1)*Rho(2)*Rho(3)*Rho(3) - 3*Rho(1)*Rho(2)*Rho(3)*Rho(4)*Rho(4) + 4*Rho(1)*Rho(2)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(2)*Rho(4) + Rho(5)*Rho(1)*Rho(2) + Rho(1)*Rho(3)*Rho(3)*Rho(3)*Rho(4) + 2*Rho(1)*Rho(3)*Rho(3)*Rho(3) - Rho(5)*Rho(1)*Rho(3)*Rho(3) + 3*Rho(1)*Rho(3)*Rho(4)*Rho(4) - 2*Rho(1)*Rho(3) - Rho(5)*Rho(1) - 2*Rho(2)*Rho(2)*Rho(2)*Rho(2)*Rho(4) + 2*Rho(2)*Rho(2)*Rho(2)*Rho(2) + Rho(2)*Rho(2)*Rho(2)*Rho(3)*Rho(3) - Rho(5)*Rho(2)*Rho(2)*Rho(2)*Rho(3) + 2*Rho(2)*Rho(2)*Rho(3)*Rho(3)*Rho(4) - Rho(2)*Rho(2)*Rho(3)*Rho(3) - Rho(5)*Rho(2)*Rho(2)*Rho(3) + 3*Rho(2)*Rho(2)*Rho(4)*Rho(4) - 2*Rho(2)*Rho(2)*Rho(4) - Rho(2)*Rho(2) - Rho(2)*Rho(3)*Rho(3)*Rho(3)*Rho(3) - 2*Rho(2)*Rho(3)*Rho(3)*Rho(4) + Rho(2)*Rho(3)*Rho(3) + Rho(5)*Rho(2)*Rho(3) - Rho(3)*Rho(3)*Rho(4) + Rho(5)*Rho(3)*Rho(4) - Rho(4)*Rho(4)*Rho(4) + Rho(4))/((2*Rho(1)*Rho(3) - Rho(4) - Rho(2) - Rho(2)*Rho(4) - 4*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(4) + 3*Rho(1)*Rho(1) + 2*Rho(2)*Rho(2) + 2*Rho(2)*Rho(2)*Rho(2) + Rho(3)*Rho(3) - 4*Rho(1)*Rho(2)*Rho(3) - 1)*(Rho(1)*Rho(1) - 2*Rho(1)*Rho(3) + Rho(3)*Rho(3) + Rho(2) + Rho(4) - Rho(2)*Rho(4) - 1));
+          AutoregressiveParameters(5) = (Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(1) - 3*Rho(1)*Rho(1)*Rho(1)*Rho(1)*Rho(3) + Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(1) + 3*Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(2) - 6*Rho(1)*Rho(1)*Rho(1)*Rho(2)*Rho(4) - 4*Rho(1)*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(1)*Rho(3)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(1)*Rho(1)*Rho(3) + Rho(1)*Rho(1)*Rho(1)*Rho(4)*Rho(4) + 4*Rho(1)*Rho(1)*Rho(1)*Rho(4) + 5*Rho(1)*Rho(1)*Rho(2)*Rho(2)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(1)*Rho(2)*Rho(2) + 4*Rho(1)*Rho(1)*Rho(2)*Rho(3)*Rho(4) - 2*Rho(1)*Rho(1)*Rho(2)*Rho(3) + 4*Rho(5)*Rho(1)*Rho(1)*Rho(2) + Rho(1)*Rho(1)*Rho(3)*Rho(3)*Rho(3) + Rho(5)*Rho(1)*Rho(1)*Rho(3)*Rho(3) - Rho(1)*Rho(1)*Rho(3)*Rho(4)*Rho(4) - 2*Rho(1)*Rho(1)*Rho(3)*Rho(4) + 3*Rho(1)*Rho(1)*Rho(3) - 3*Rho(5)*Rho(1)*Rho(1) - 2*Rho(1)*Rho(2)*Rho(2)*Rho(2)*Rho(2) + 2*Rho(1)*Rho(2)*Rho(2)*Rho(2)*Rho(4) - 2*Rho(1)*Rho(2)*Rho(2)*Rho(2) - 5*Rho(1)*Rho(2)*Rho(2)*Rho(3)*Rho(3) - 2*Rho(5)*Rho(1)*Rho(2)*Rho(2)*Rho(3) + Rho(1)*Rho(2)*Rho(2)*Rho(4)*Rho(4) + 3*Rho(1)*Rho(2)*Rho(2) + 2*Rho(1)*Rho(2)*Rho(3)*Rho(3)*Rho(4) - 4*Rho(1)*Rho(2)*Rho(3)*Rho(3) + 4*Rho(5)*Rho(1)*Rho(2)*Rho(3) - 2*Rho(1)*Rho(2)*Rho(4)*Rho(4) + 2*Rho(1)*Rho(2)*Rho(4) - Rho(1)*Rho(3)*Rho(3)*Rho(3)*Rho(3) - 2*Rho(1)*Rho(3)*Rho(3)*Rho(4) + Rho(1)*Rho(3)*Rho(3) - 2*Rho(1)*Rho(4) + Rho(2)*Rho(2)*Rho(2)*Rho(2)*Rho(3) + Rho(5)*Rho(2)*Rho(2)*Rho(2)*Rho(2) - 2*Rho(2)*Rho(2)*Rho(2)*Rho(3)*Rho(4) + 2*Rho(2)*Rho(2)*Rho(2)*Rho(3) + Rho(2)*Rho(2)*Rho(3)*Rho(3)*Rho(3) - 2*Rho(2)*Rho(2)*Rho(3)*Rho(4) - 2*Rho(5)*Rho(2)*Rho(2) + 2*Rho(2)*Rho(3)*Rho(3)*Rho(3) + 2*Rho(2)*Rho(3)*Rho(4) - 2*Rho(2)*Rho(3) - Rho(5)*Rho(3)*Rho(3) + Rho(3)*Rho(4)*Rho(4) + Rho(5))/((Rho(1)*Rho(1) - 2*Rho(1)*Rho(3) + Rho(3)*Rho(3) + Rho(2) + Rho(4) - Rho(2)*Rho(4) - 1)*(2*Rho(1)*Rho(3) - Rho(4) - Rho(2) - Rho(2)*Rho(4) - 4*Rho(1)*Rho(1)*Rho(2) + 2*Rho(1)*Rho(1)*Rho(4) + 3*Rho(1)*Rho(1) + 2*Rho(2)*Rho(2) + 2*Rho(2)*Rho(2)*Rho(2) + Rho(3)*Rho(3) - 4*Rho(1)*Rho(2)*Rho(3) - 1));
+      otherwise
+        AutocorrelationMatrix = eye(p);
+        for i=1:p-1
+            AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),i);
+            AutocorrelationMatrix = AutocorrelationMatrix + diag(Rho(i)*ones(p-i,1),-i);
+        end
+        AutoregressiveParameters = AutocorrelationMatrix\Rho;
+    end
+    InnovationVariance = Variance * (1-AutoregressiveParameters'*Rho);
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