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RE: st: Grouping income variables- RECODE COMMAND

From   "Antonio Rodriguez Andres" <>
To   <>
Subject   RE: st: Grouping income variables- RECODE COMMAND
Date   Tue, 4 Feb 2014 15:54:32 +0200


What you are suggesting is to use the command mi?

mi set wide
mi register imputed hincome
mi impute reg hincome  (which variables should be included) all the variables included in the original regression?
I know that mi can be used with xtmixed.


-----Original Message-----
From: [] On Behalf Of Maarten Buis
Sent: Tuesday, February 04, 2014 3:41 PM
Subject: Re: st: Grouping income variables- RECODE COMMAND

On Tue, Feb 4, 2014 at 1:48 PM, Antonio Rodriguez Andres wrote:
> Thank you very much for your feedback. What I did is the following

Rich quotes Allison in that handout:

This technique of including a missingness indicator variable is "remarkably simple and intuitively appealing." But unfortunately, "the method generally produces biased estimates of the coefficients."

The logic of why that leads to biased estimates is discussed in the link I gave before. So my recommendation is still "do not do that".

> **dummy indicator for missing income values
> gen xhincome=hincome
> replace xhincome= 29304.99 if missing(hincome) gen md=0 replace md=1 
> if xhincome! =hincome
> xtmixed dprt age age2 gender married separated divorced widowed eduyrs 
> ichldhm md lhincome ihealth iuemp5yr iuemp12m rgdp06[pw=dweight] if 
> md==0 || cntry: gender , mle
> But I still got the same message, the md indicator variable is dropped. How can İ estimate the model controlling for missing values in income?

a) I repeat: the method you want to use does _not_ control/adjust/solve/remove for missing values. So you should not use it.

b) you probably still have the same missing values on lhincome, so in the estimation sample md is still a constant. (I am delibaretly not telling you how to solve it, as I don't want you to do something that is wrong)

-- Maarten

Maarten L. Buis
Reichpietschufer 50
10785 Berlin

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