# Random parameter linear regression models

I am trying to estimate a random parameter linear regression models in LIMDEP (Hildreth, Houck and Swamy's Random Coeffficents Model). Since my data is x-sectional, therefore, I am not using Pds command (being x-sectional data), my commands are as follows:

regress;lhs=......
;rhs= one,....................
;Fcn= .......................
;RPM
;Pts=50
;Halton\$


LIMDEP works in two step: 1. In step-one LIMDEP use OLS and comes up with starting values for ramdom parameter model and outputs an initial model. My initial model(starting value) as R-Squred of 0.5. It also gives log-likelihood for starating model which in my case is -6600. 2. Instep-2 LIMDEP uses results from OLS model to estimate random parameter regression models. In this step LIMDEP uses maximum likelihood estimates, therefore it outputs log-likelihood at zero (-6900) and at convergence is (-6700).No more R-Squred values in step-2 as LIMDEP is using different technique. 3. I am having issue of the overall fit of the model. If I try to calculate Rouhe-Squred, it is less than 0.02 which is not very appealing. On the other hand I see that starting models used by LIMDEP to estimate random parameter linear regrssion models has an R-Squred of 0.5 which is good enough. In seceond step when LIMDEP gives final results it does not show R-Squred, instead it gives log-likelihood at zero (-6900) and at convergence (-6700).I have 7 total significant variable (four random parameter) all having T-value >2 and P-value<0.001).

My question is how should I report my overall fit and is my model OK.

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Is there a question in here somewhere that I missed? –  Dason Jan 7 '12 at 3:36
A well-formatted and thoughtful question is more likely to draw the attention of other users. Please, work on the quality of your post (there's some hint on our FAQ and the editing help guide), and I'll second @Dason's comment: What's the question? In the absence of action on your part, we'll have to close this post as non-constructive. –  chl Jan 7 '12 at 8:57
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