Thanks a lot for the quick response. I appreciate it

I am working on risky steady state and need second order moments to iterate until they converge. I am, using the simplest example, trying see the mapping between the dynare codes you have posted on your website SGU_2004.mod and the NEOCLASSICAL_MODEL_RUN.M that uses the SGU package. When the autocorrelation coefficient is set to 0.

- PHO=0 ar1 coef on exogenous shocks

FOR SGU:______________________________*FOR DYNARE SGU_2004.mod*

**Steady States:**

log(kss)=-1.7932______________________________log(kss)=-1.7932

log(css)=-0.8734______________________________log(css)=-0.8734

**Policy Functions**

c_hat=0.2525k{t}+0.8417*a{t}_____________*c_hat=0.2525k*{t}+0.8417*eps{t}

k{t+1}=0.4191k{t}+1.3970*a{t}______________*k*{t+1}=0.4191k{t}+1.3970*eps{t}

**Covariance Matrix**

var(c_hat)=0.8595____________________________var(c_hat)=0.8595

var(k_hat)=2.3676____________________________var(k_hat)=2.3676

var(a_hat)=1_________________________________var(a_hat)=1

cov(k_hat,a_hat)=0____________________________cov(k_hat,a_hat)=0

**Steady States:**

log(kss)=-1.7932_____________________________log(kss)=-1.7932

log(css)=-0.8734______________________________log(css)=-0.8734

**Policy Functions**

c_hat=0.2525k_{t}+1.0496*a_{t} _____ ____c_hat=0.2525*k_{t}+0.997161*at_{t}+1.049643*eps_{t}

here RHO*1.0496=0.997161 DIFFERENT REPRESENTATION SO IT IS ok

k_{t+1}=0.4191k_{t}+0.8755*a_{t}_______**k*{t+1}=0.4191k_{t}+0.831683*at_{t-1}+0.875456*eps_{t}

HERE ALSO SAME LOGIC

**Covariance Matrix**

var(c_hat)=20.2261_________________________var(c_hat)=20.2261

var(k_hat)=22.1525_________________________var(k_hat)=22.1525

var(a_hat)=14.1732_________________________var(a_hat)=10.2564

cov(k_hat,a_hat)=10.2564____________________cov(k_hat,a_hat)=???

Sorry for writing this long. But I have this huge model with bunch of other variance and covariances and i was wondering if the SGU mom.m function and dynare one is actually different