st: Mata maximum likelihood error on trivial regression

[David Chan <david.c.chan@gmail.com>]

Hi all,

I'm having problems with doing maximum likelihood in Mata. Even in the
most basic setup, I'm getting the error,

"could not calculate numerical derivatives -- flat or discontinuous
region encountered
r(430)."

I'm almost certain that this is not because of problems with the data
or model, but it probably has something to do with a naive programming
error. I'm simulating my own data, and I'm testing out the program on
a very simple linear regression model,

y=Ey+zeta_k+epsilon,

where epsilon is a random, normally distributed term, and Ey and
zeta_k are both observed regressors (both with coefficient 1).

After simulating data according to this model, simple regression,

> reg y Ey zeta_k"

works fine. Also when I code up the trivial MLE in Stata as an ado
program, I have no problems:

> program mynormal_lf
>     args lnfj mu sigma
>    qui replace `lnfj'=ln(normalden($ML_y1,`mu',`sigma'))
> end
> ml model lf mynormal_lf (mu: lnlos = Ey zeta_k) /sigma
> ml maximize

However, when I code it up in Mata, using the code below, I always get
the error message, even when I set the initial values at the true
parameters:

> mata:
> void mydynamic_lf(transmorphic scalar ML, real rowvector b, ///
>                  real colvector lnfj)
> {
>     real colvector y, xb
>     real colvector sd
>     y=moptimize_util_depvar(ML,1)
>     xb=moptimize_util_xb(ML,b,1)
>     j=moptimize_util_eq_indices(ML,2)
>     sd=exp(b[|j|])
>     lnfj=ln(normalden(y,xb,sd))
> }
> end
> ml model lf mydynamic_lf() (mu: lnlos = Ey zeta_k) /sigma
> ml maximize

I've tried renaming the variables within Mata, as well as changing the
variable declaration (e.g., calling "sd" a scalar rather than a
colvector), but nothing seems to work.

Does anyone know what I'm doing wrong?

Dave
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