Thank you very much Bobby and Kit,
I got the point and the answer to my question.
Below is the syntax to estimate MLEs using Mata -optimize()- in linear
regression.
Nicola
/* Linear regression using Mata optimize */
sysuse auto, clear
local xlist mpg _cons
mata
mata clear
st_view(X=0,.,("mpg"))
st_view(y=0,.,("price"))
X=X,J(rows(X),1,1)
void mlreg(todo, p, y, X, lf, g, H)
{
b = p[1,(1::cols(X)) ]
s= p[1,(cols(X)+1)]
lf = lnnormalden( (y-X*b'):/s ) :- ln(s)
}
S = optimize_init()
optimize_init_evaluator(S, &mlreg())
optimize_init_evaluatortype(S, "v0")
optimize_init_params(S, J(1,cols(X)+1,3))
optimize_init_argument(S, 1, y)
optimize_init_argument(S, 2, X)
optimize_init_which(S,"max")
p = optimize(S)
vhat = optimize_result_V(S)
st_matrix("b", p[1,(1::cols(X))])
st_matrix("V", vhat[(1::cols(X)),(1::cols(X))])
end
matrix colnames b = `xlist'
matrix colnames V = `xlist'
matrix rownames V = `xlist'
ereturn post b V , dep(price)
ereturn di
xtmixed price mpg, ml
exit
On Feb 10, 2009, at 3:52 PM, Roberto G. Gutierrez, StataCorp wrote:
Nicola Orsini <[email protected]> asks:
My question is why I'm not getting the correct variance-covariance
matrix in
the example below (simple linear regression) when using
optimize_result_V(S). Any thoughts on this?
I haven't looked at the Mata code closely, but I see that Nicola is
comparing
his program to -regress-. -regress- gives OLS standard errors,
which are
_not_ maximum likelihood (ML). They are REML. The difference is
one of
dividing by n instead of (n-p), but it is enough to throw off the
standard
errors.
If Nicola wants to test his optimize() code, he should try comparing
with
-xtmixed, mle-, which fits ML linear regression as a degenerate case.
--Bobby
[email protected]
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
