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[no subject]
xtset country idno
regress depression children age //OLS
regress depression children age, cluster(country) // OLS
xi: regress depression children age i.country //FE
xtreg depression children age, re i(country) // Random effects
Ýs the syntax used to generate your results?
Regards
Antonio
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
[email protected]
Sent: Wednesday, February 05, 2014 12:08 PM
To: [email protected]
Subject: st: -xtmixed- and multilevel data [Was: Grouping income variables-
RECODE COMMAND]
In addition to having issues with how to deal with grouped income data with
missing values, I note that Antonio Rodriguez Andres
<[email protected]> is using European Social Survey data -- pooling
data for 23 countries, with a large number of respondents per country, and
then fitting a mixed model.
I suggest that Antonio proceed with caution with this modelling approach.
The number of countries is "small" and so country effects (coefficients on
country fixed effects or random effect variances) may not be reliably
estimated.
For a review of the issues, see:
'Regression analysis of cross-national differences using multi-level data: a
cautionary tale', Working Paper 2013-14. Colchester: Institute for Social
and Economic Research, University of Essex.
https://www.iser.essex.ac.uk/publications/working-papers/iser/2013-14
Related: see also "A Monte Carlo analysis of multilevel binary logit model
estimator performance" presented at the 2013 UKSUG:
http://www.stata.com/meeting/uk13/abstracts/
Stephen
------------------
Stephen P. Jenkins <[email protected]>
Date: Tue, 4 Feb 2014 14:48:54 +0200
From: "Antonio Rodriguez Andres" <[email protected]>
Subject: RE: st: Grouping income variables- RECODE COMMAND
Dear Maarten
Thank you very much for your feedback. What I did is the following
http://www3.nd.edu/~rwilliam/stats2/l12.pdf
*Create income midpoints
recode hinctnt (1=900) (2=2700) (3=4800) (4=9000) (5=15000) (6=21000)
(7=27000) (8= 33000) (9=48000) (10=75000) (11=105000) (12= 175200) ,
gen(hincome) replace hincome=. if hinctnt==77 | hinctnt==88 | hinctnt==99
gen lhincome=log(hincome)
**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 Ä° estimate the model controlling for missing values in income?
Antonio
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