Hello there,
I try again with my question ☺
I am using the optimize command in mata to minimize a criterion function similar to the code shown below.
After estimating the parameters in the minimization I am in doubt how to use the "optimize_result_V" to obtain st.e.
– as I do not maximize a likelihood but minimize the distance of sample moments to theoretical moments.
Anybody who can offer guidance on this topic, i.e. how to obtain st.e.?
Best
Miriam
b=(0,0,0,0) //starting values for parameter estimates
data=(y,X)
variance=variance(data)
c=(variance[1,1], variance[2,1], variance[3,1], variance[4,1])' //sample covariances
W= I(4,4) //weighting matrix
f1=(0,1,1,0\
0,1,0,0\
0,0,0,1\
0,0,0,1
)
f2=(2,0,0,0\
2,0,0,0\
0,0,0,0\
0,0,0,0
)
f2a=(0,0,0,1\
0,0,0,1\
0,0,0,0\
0,0,0,0
)
void evaluator_gmm(todo, p, crit, g, H)
{
external W
external c
external f1
external f2
external f2a
crit=(c-f1*p'-(f2*p'):*(f2a*p'))’*W*(c-f1*p'-(f2*p'):*(f2a*p'))
}
S=optimize_init()
optimize_init_evaluator(S,&evaluator_gmm())
optimize_init_which(S,"min")
optimize_init_evaluatortype(S,"v0")
optimize_init_params(S, b)
//optimize_init_technique(bhhh)
ehat=optimize(S)
)
End
optimize_result_V(S)
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