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Re: st: Multivariate GMM using Mata's "optimize" command: conformability error.


From   Austin Nichols <austinnichols@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Multivariate GMM using Mata's "optimize" command: conformability error.
Date   Wed, 11 Dec 2013 12:57:01 -0500

Daniel Hubbard <danieldh@umich.edu>:

Why not simply use -gmm- now that it is available?
But perhaps Mata cannot see your variables?  Try e.g.:

 mata: ;
     pi = st_matrix("pi_out");
     V = st_matrix("V_out");
 invV = invsym(V);
 x = J(1,12,0);
 void i_crit(todo,x,crit,g,H) {;
  external pi ;
  external invV;
  beta = x[1..4];
  gamma = x[5..8];
  lambda = x[9..12];
  m = pi - (vec(beta :* I(4)))' - (gamma # lambda);
  crit = (m*invV*m');
 };
 init=J(1,12,0);
 S=optimize_init();
 optimize_init_evaluator(S, &i_crit());
 optimize_init_which(S, "min");
 optimize_init_evaluatortype(S, "d0");
 optimize_init_params(S, init);
 p=optimize(S);

On Tue, Dec 10, 2013 at 5:23 PM, Daniel Hubbard <danieldh@umich.edu> wrote:
> Hi everyone,
>
> I am trying to use GMM to run a test of the validity of fixed effects
> using "optimize" in Mata (in Stata 12.1). The program is based on the
> one found in Austin Nichols' presentation found at
> http://repec.org/snasug08/nichols_gmm.pdf ; the test is based on
> section 2.2 of George Jakubson's 1990 paper "Estimating the Union Wage
> Effect", found at http://www.jstor.org/stable/10.2307/2297947 (a
> Google Scholar search does not turn up a free version of the paper -
> sorry!).
>
> In the first stage (omitted here, though I can add it if it would be
> helpful), I estimate a 16-element row vector called "pi_out" and a
> 16x16 matrix called "V_out", which I import in the second stage. My
> second-stage code is as follows:
>
> /*** SECOND STAGE ***/
> mata: ;
>     pi = st_matrix("pi_out");
>     V = st_matrix("V_out");
>     invV = invsym(V);
>     I = I(4);
>     x = J(1,12,0);
>     void i_crit (todo,x,crit,g,H) {;
>         beta = x[1..4];
>         gamma = x[5..8];
>         lambda = x[9..12];
>         m = pi - (vec(beta :* I))' - (gamma # lambda);
>         crit = (m*invV*m');
>     };
>     init=J(1,12,0);
>     S=optimize_init();
>     optimize_init_evaluator(S, &i_crit());
>     optimize_init_which(S, "min");
>     optimize_init_evaluatortype(S, "d0");
>     optimize_init_params(S, init);
>     p=optimize(S);
> end;
> /*** END SECOND STAGE ***/
>
> When I get to the last line of this (running "optimize"), I receive
> "i_crit():  3200  conformability error" at the top of the traceback
> log. I tried to replicate the i_crit function by hand (running each of
> the five lines inside it individually), and it worked perfectly; it
> created a 16-element vector for "m" and a scalar for "crit", as
> expected.
>
> These aren't the correct values of "m" and "crit", because they aren't
> optimized (all the Greek letter variables are still zero), but
> everything adds and multiplies together just as it is supposed to, so
> the source of the conformability error isn't clear to me. Is there
> something particular to "optimize" that is incorrectly specified in my
> code? I'm still in the process of learning Mata, which I am largely
> doing by trial and error.
>
> Thank you for your help; let me know if anything is unclear. The full
> traceback is posted below, if it is helpful.
>
> Daniel Hubbard
> Education Policy Initiative
> University of Michigan, Gerald Ford School of Public Policy
>
> :     p=optimize(S);
>                 i_crit():  3200  conformability 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
> (0 lines skipped)
>
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