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Re: st: panel data analysis advice


From   Theophilus Dapel <[email protected]>
To   Statalist <[email protected]>
Subject   Re: st: panel data analysis advice
Date   Mon, 10 Mar 2014 21:21:56 +0000

Please permit me to also seek for assistance.

I have a balanced but unequally spaced panel dataset:

1980

1985

1992

1996

2004 and

2010.

How do I get around this?

Thanks
On 10 Mar 2014, at 21:09, Robert Paul <[email protected]> wrote:

> Dear Statalist,
> 
> 
> I have demographic and treatment information for patients chronic disease (N=60,000). I got permission to link a subset of my data to income data (18.5%). For this subset I have 20 years panel data.
> The data in long format looks 
> Id     year  income age …
> 1      1990  100   45
> 1      1991  110   45
> 1      1992   125   45
> 1      1993  132   45
> .
> .
> .
>  
> My aim is 
> a-  to estimate the effect of demographic, treatment, and being chronic disease patient, on patient’s income; and
> b-  to evaluate differences in income between patients and the general population (when linked to control population)
>  
> to address these issues I plan  
>  
> a-  to run a Fixed and Random effects model , to start with  then run  Hausman test …
>  
> b-  I will also get a control group for my data - (from general population without chronic disease -matched by demographic vars)  --- for this I plan to use Hausman-Taylor that utilizes the vars as instruments and provide  parameter estimate for time-invariant variable (major variable of interest – chronic disease patient or not)
>  
> 
>  
> Dependent variable – log equivalized income
> RHS vars – age at end of follow-up,  age^2, age at diagnosis,  treatment type
>     1. Run  xtreg logincome age age_square age at diagnosis treatment type dummies . .  , fe
>     2. xtreg logincome age age_square age at diagnosis treatment type dummies . . . .  , re
>     3. xtreg logincome age age_square age at diagnosis treatment type dummies . . . , re vce(robust)  or
>     4. xtreg logincome age age_square age at diagnosis treatment type dummies . . . , re vce(cluster id)
>  
> The aim of using vce or cluster is to produce consistent VCE estimator when the disturbances are not identically distributed over the panels. 
>  
>  
> 5.  ** Hausman Taylor estimation
>   
> . xthtaylor  logincome age age_square age at diagnosis treatment_type dummies, endog(age treatment type dummies)
>  
> My question, as I am new to panel data analysis, is if I am doing the right way to address my question.
> 1.  Do I need to calculate weights because I am using a subset of the population? If yes, how do I do that?
> 2.  I am not sure – probably using dynamic models would be more appropriate 
> 3.  I need advice on my analysis procedure. This is of critical importance for my project.  I appreciate your valuable comments.
> Thanks
> 
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