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From |
Ed Blackburne <[email protected]> |

To |
[email protected] |

Subject |
st: ML Hold |

Date |
Tue, 07 Sep 2004 11:37:22 -0500 |

I understand ML hold is undocumented...

I have looked at http://www.stata.com/help.cgi?ml_hold and successfully run the example code.

However, I am running into a problem with my exact model, probably because I do not fully understand the mechanics of ML hold/unhold.

For example, is ml hold/unhold the "Stata way" of tackling a model with an unidentified parameter such as a panel error correction model:

model 1: y_it=theta_i*(y_it-1-beta*x_it-1)+zeta_i*d.x_it

This is a restricted (with nonlinear restrictons of gamma_i/theta_i=beta) model of

model 2: y_it=theta_i*y_it-1-gamma_i*x_it-1+zeta_i*d.x_it

Currently, the way to solve for parameters of model 1 is with interated ML procedures, conditioning on beta, then other parameters, etc, until convergence. It is easy enough to program with nested procedures and while loops for convergence, etc.

However, isn't this purpose of ML hold/unhold (a real, non-rhetorical question ;) )?

When I change the example program referenced above, I cannot get any sort of convergence. At first I thought this was because of my panel type model. So I changed the procedures to (using auto.dta) estimate the (almost) equivalent of:

model 3: regress price mpg weight

using the conditional models in the example program (replaced with the familiar `lnf'=ln(normd(......) to estimate OLS rather than negative binomial.

The problem is when I condition on anything other than constant, I cannot get the model to estimate anything near correct results (I'm talking order of magnitude, not significant digits). I believe the problem is the way I am passing the `xb' estimated to the conditional likelihood, but I really don't know at this point.

Any takers??? I'll clip and post some code if in fact the problem can be solved elagently with ML hold/unhold.

Thanks for your time,

Ed

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