st: Re: Re: 2sls with multiply-imputed data sets

["Rodrigo A. Alfaro" <raalfaroa@gmail.com>]

The idea sounds good, but I didn't check the code. R


----- Original Message ----- 
From: "Viola Angelini" <viola.angelini@unipd.it>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, May 29, 2007 3:57 AM
Subject: st: Re: 2sls with multiply-imputed data sets



> Thanks Rodrigo!
> As regards the Hausman test, the only solution I can think of is to use 
> the regression-based form of the test where I combine the results of the 
> regression only at the second stage.
> Does it make any sense or would it be better to just have 5 different 
> Hausman tests?
> Suppose that the multiply-imputed dataset is stored in 5 separate files: 
> mydata1.dta, mydata2.dta, mydata3.dta, mydata4.dta, mydata5.dta
> The Stata code would be the following:
>
> forvalues i=1(1)5 {
> use mydata`i'.dta
> regress y2 z1 z2 x1 x2
> predict res if e(sample), resid
> save, replace
> }
> clear
> set memory 500m
> mimstack, m(5) so("id") nomj0 istub(mydata)
> mim: regress y1 y2 x1 x2 res, cluster(sampid2)
>
> [y2 is the endogenous variable and z1, z2 are the excluded instruments]
>
> Best
>
> Viola
>
>
>
>
>
>
> -------------------------------------------------------------------
> From  "Rodrigo A. Alfaro" <raalfaroa@gmail.com>
> To  <statalist@hsphsun2.harvard.edu>
> Subject  st: Re: 2sls with multiply-imputed data sets
> Date  Fri, 25 May 2007 18:23:15 -0400
>
>
> We had a similar discussion on the list this week. In that case, the topic 
> was the R2 for Multiple Imputation (MI). Maarten proposed (for R2 case) to 
> compute the geometric average instead of arithmetic one, based on Donald 
> Rubin's reply somewhere else. Hansen J test is asymptotically distributed 
> as chi-square, maybe a similar suggestion applies for your case.
>
> My own suggestion for the R2 was to report your simple average and write a 
> small note with the min/max R2 along your regressions. In your case, I 
> suggest to analyze more in deep the figures for Hansen J tests and the 
> p-values associated with these. I think that is perfectly OK to have 
> pvalues of 0.01 0.008, etc. (similar magnitud)... and I don't expect to 
> see very different values for Hansen J test as well. If so... then you 
> have problems with the model and/or the method of MI.
>
> All this works if your # of missing over the total observations is few and 
> if you imputed all the variables (including the variables used in the 
> first step) at once. Finally, MI methods are based on simulations then in 
> practice I generate more than 5 datasets and play with some combinations 
> of 5 datasets (2nd to 6th, etc) and with more datasets (8, 10 or 12) to 
> see if the results change.
>
> R
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