<>
Robby G. suggested a d-f adjustment (for n-k rather than n) was the
issue. It isn't, as the optimize() matrix differs from OLS vce by a
very large factor. What you need to do to get OLS (small-sample)
standard errors is multiply the optimize() covariance matrix by [2
s^2]:
-----------
/* Linear regression using Mata optimize */
sysuse auto, clear
local xlist mpg cons
mata
st_view(x=0,.,("mpg"))
st_view(y=0,.,("price"))
x=x,J(rows(x),1,1)
void olsest(todo, b, y, x, lf, g, H)
{
e = (y-x*b')
lf = -(e'*e)
}
S = optimize_init()
optimize_init_evaluator(S, &olsest())
optimize_init_evaluatortype(S, "v0")
optimize_init_argument(S, 1, y)
optimize_init_argument(S, 2, x)
optimize_init_params(S, J(1,cols(x),0))
optimize_init_which(S,"max")
betahat = optimize(S)
vhat = optimize_result_V(S)
s2 = -1 * optimize_result_value(S) / (rows(y) - cols(x))
vhat = vhat * 2 * s2
st_matrix("b", betahat)
st_matrix("V", vhat)
end
matrix colnames b = `xlist'
matrix colnames V = `xlist'
matrix rownames V = `xlist'
mat list V
ereturn post b V , dep(price)
ereturn di
reg price mpg
mat list e(V)
--------------
Presumably if you divided by n rather than n-k in computing s^2, you
would then agree with -xtmixed, mle- or -ivreg2 price mpg- standard
error estimates.
Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
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