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Re: st: maximum likelihood estimation similar to Poi 2002
From
Alex Olssen <[email protected]>
To
[email protected]
Subject
Re: st: maximum likelihood estimation similar to Poi 2002
Date
Tue, 29 Mar 2011 23:04:52 +1300
Thanks for all your help on these topics Brian. I got the ML
estimation to work fine and verified that for the simplest model it
reproduces the coefficients of OLS equation by equation, just like
-sureg-. I had problems getting -ml trace on- to work but -set trace
on- was sufficient for me to debug relatively quickly.
Kind regards,
Alex
On 29 March 2011 11:41, Brian P. Poi <[email protected]> wrote:
> On 3/28/2011 4:46 PM, Alex Olssen wrote:
>>
>> Thanks a lot for pointing out e(ll), that should help a lot.
>>
>> I just remembered that I had one other problem with -nlsur-
>>
>> It has to do with the number of parameters that I can estimate. I
>> have 35 years of annual observations on 4 shares. I want to use 12
>> regressors in each of 3 equations - one dropped for estimation.
>>
>> -nlsur- doesn't like this. But to my mind this should be possible.
>> Dropping one equation for estimation I no longer have any cross
>> equation restrictions and I estimate as if by OLS equation by
>> equation. Clearly with 35 observations I can estimate a simple model
>> with 12 regressors by OLS. Alternatively think of the simple
>> multivariate case.
>>
>> Further more in R I can estimate a model with 3 equations with 12
>> regressors each using 35 years of annual observations on 4 shares.
>>
>
> -nlsur- uses a simple counting rule to determine identification, and it
> tends to be conservative because it doesn't consider cross-equation
> restrictions (or, equivalently, that the same parameter appears in multiple
> equations). In certain situations it may be possible to fit models with
> fewer observations than -nlsur- thinks you need -- and in those cases -ml-
> is one alternative. However, based on my experience with AIDS and similar
> models, the more observations and variation in the data, the better.
> Usually when people have non-convergence issues in fitting these models, it
> often ends up being a matter of not having enough data to identify all the
> parameters.
>
> -- Brian Poi
> -- [email protected]
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