Brute Force Optimization

Hi,

I have my model below:

Y_{i,t}= a*Y_{i,t-1}+ b*Y_{j,t}+ +c*Y_{j,t-1}+ mu_{i} + e_{i,t};
e_{i,t}= d*e_{j,t} + v_{i,t};
v_{i,t}= e*v_{i,t-1} + err_{i,t}

mu_{i} follows normal with mean 0 and variance sigma_{mu_{i}}. err_{i,t} follows std normal distribution and I have the parameter restrictions for stability of the entire system.

I have to estimate the parameters {a,b, c, d, e and sigma_{mu_{i}}. I understand that I have to do it using brute force; but I have never done such thing in my life. So it would be very helpful if anyone can give me some references on how I can approach the problem.

Thanks so much!