Re: st: mata optimize problem

[Austin Nichols <austinnichols@gmail.com>]

Michael Ralph M. Abrigo <mmabrigo@gmail.com> :

I have not read through this, but you should put the dimensions of
every matrix in as a comment, and also print out the matrices once,
and then maybe you will see where the error is.

On Sat, Jul 3, 2010 at 2:08 AM, Michael Ralph M. Abrigo
<mmabrigo@gmail.com> wrote:
> Good day statalisters!
> I am practically new in programming, especially in Mata. I'm trying to
> implement Lambert, Brown and Florax's (2010) poisson spatial lag model
> ("A two-step estimator for a spatial lag model for counts: Theory,
> small sample performance and an application", Urban and Regional
> Economics) in Stata using full-information ML (instead of the two-step
> LIML). However, I got stuck because of a conformability error which I
> cannot trace (I was able to generate the matrices interactively using
> ad hoc values). I was hoping I can do the FIML first before I venture
> to their two-step estimator.
> Any hint will be greatly appreciated.
> Below is my code and the error messages.
> Cheers,
> Michael
> drop _all
> ** (1) Set version; Open file
> version 11.0
> webuse "dollhill3.dta", clear
> ** (2) Generate constant; Local variables
> gen cons = 1
> local y deaths
> local xlist smokes pyears cons
> ** (3) Generate weight matrix
> sort pyears
> gen xcoor = _n
> gen ycoor = _n
> spwmatrix gecon xcoor ycoor, wname(W) wtype(bin) ///
>    dband(0 1000) rowstand mata replace
> ** (4) Optimize using Mata
> mata
>   void spmlpoisson (
>     real scalar todo,
> real rowvector rho_b,
> real colvector lndensity,
> real matrix g,
> real matrix H)
> {
>       external y, X, W, I
>  transmorphic A_inv
>  real colvector Xb_sar, mu_sar
>  real rowvector b
>  real scalar rho
>       rho_b = rho, b
> A_inv = cholinv(I - rho * W)
> Xb_sar = A_inv * X * b'
>         mu_sar = exp(Xb_sar)
> lndensity = (y :* Xb_sar) - mu_sar - lnfactorial(y)
> }
>   st_view(y = ., ., "`y'")
>   st_view(X = ., ., tokens("`xlist'"))
>   fh = fopen("W", "r")
>     W = fgetmatrix(fh)
>    fclose(fh)
>   I = I(cols(W))
>   S = optimize_init()
>   optimize_init_evaluator(S, &spmlpoisson())
>   optimize_init_which(S, "max")
>   optimize_init_evaluatortype(S, "d0")
>   optimize_init_params(S, J(1, cols(X) + 1, 0))
>   rho_b = optimize(S)
>                        *:  3200  conformability error
>            spmlpoisson():     -  function returned error
>       opt__calluser0_d():     -  function returned error
>       opt__d0_calluser():     -  function returned error
>     deriv__call1user_d():     -  function returned error
>  _deriv__compute_value():     -  function returned error
>                 _deriv():     -  function returned error
>        opt__eval_nr_d0():     -  function returned error
>              opt__eval():     -  function returned error
> opt__looputil_iter0_common():     -  function returned error
> opt__looputil_iter0_nr():     -  function returned error
>           opt__loop_nr():     -  function returned error
>               optimize():     -  function returned error
>                  <istmt>:     -  function returned error
> r(3200);
>
>
> end

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