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Re: st: RE: Frontier code- r(1400) error

Subject   Re: st: RE: Frontier code- r(1400) error
Date   Wed, 6 Apr 2011 09:58:19 +0300 (EEST)

Dear Nick,

I tried the Cobb Douglas function at first then I include the other
variables to the model one by one. The results are much more better.
Another thing I want you to ask is about the distribution assumption. When
I use the exponential distribution, the results are significant even if
the iteration number is small (for instance: 1000). However, when I use
the half normal distribution with 1000 iteration, some of the parameters
appear as dots in the results. Sometimes increasing the iteration number
works in such cases. Is it logical to increase the iteration number? Or if
the results appear as dots even with the 1000 iteration, then it is not
needed to be continued?


> Your original posting was
> Evidently, that guess of 29 observations was wrong. You are still left
> with the suggestion that your model appears too complicated and that
> one strategy is to simplify it radically. When you get a model that
> does not produce estimates, you can complicate it step by step to see
> where the problem lies.
> Nick
> 2011/4/2  <>:
>> Dear Nick and Gordon
>> Thank you for your advices. But Gordon says that "you have only 29
>> observations". I might have expressed myself in an incorrect way. I have
>> 4
>> output variables, 3 input price variables for the 29 firms. Actually I
>> have 208 observations. It seems enough to estimate the frontier, isn't
>> it
>> ?
>> Thank you
>> Hande
>>> Nick's answer is correct.  You have 27 parameters plus the additional
>>> parameters for the efficiency error distribution and only 29
>>> observations.  This will never produce a satisfactory result.
>>> Translog frontier models can be difficult to estimate under the best
>>> of circumstances without trying to over-determine the frontier.  You
>>> should start by estimating the basic log-linear Cobb-Douglas form
>>> (dropping all of the interaction terms) and then introduce
>>> interactions individually and very carefully.  Even then it is
>>> unlikely that you will get any convincing results with such a small
>>> sample.
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