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Re: st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?
Date   Fri, 6 Jul 2007 13:06:56 -0400

Jessica <jholmes@middlebury.edu> :
I don't know about the tests for het or clustering after -xtprobit-
and -xttobit- (perhaps someone else on the list can address these).
-ssc install clogithet- gives you a test for heteroskedasticity in a
fixed-effects logit model, described in

Hole, A.R., 2006. Small-sample properties of tests for
heteroscedasticity in the conditional logit model. Economics Bulletin
3, 1-14.
http://economicsbulletin.vanderbilt.edu/2006/volume3/EB-06C20063A.pdf

However, it seems to me your reviewer missed the main problem, that
-xttobit- is the wrong model. You seem to be estimating ln(real y + 1)
as a function of X with a lower limit of 1. But ln(1)=0 --how many
left-censored observations does Stata report? Is it the same as the
number of obs where y=0?.  Or perhaps you are estimating ln(real y +
exp(1)) as a function of X with a lower limit of 1, where the lower
limit makes more sense for a depvar y>=0.  This still  assumes that
the values observed at zero really should be mostly negative, and the
underlying distribution is normal, but this is unlikely to be the
case.  What you really want is a GLM model or -poisson- type model,
right? See e.g.
http://www.stata.com/statalist/archive/2007-04/msg00549.html
http://www.stata.com/statalist/archive/2006-12/msg00466.html
I would recommend you replace -xttobit- using lnreal1 as the depvar
with -xtpoisson- using real as the depvar.

In general, adding some number to y, taking the log, and running a
-tobit- type model is unjustifiable.  For one, your estimates will
differ depending on what number you specify, and there is rarely a
theoretical justification for one number over another.

On a more substnantive point, are there no fixed time effects in your
model?  To the extent that marginal tax rates and the deductability of
giving varies over time, you would at least want to include year
effects, I think, even if you can't estimate first-dollar marginal tax
rates for individuals (to get a plausibly exogenous tax-price of
giving).


On 7/6/07, Holmes, Jessica <jholmes@middlebury.edu> wrote:
Hope someone can help! I am using a panel data set that looks at
charitable giving for 22,000 individuals over 15 years.

I use a random effects Probit to predict whether an individual donates
in a given year and a random effects Tobit to predict how much he/she
gives each year:

xtprobit donate male yrsgrad yrsgradsq mar alumnicloserelative
med_income artsent bankfin commedia comptech consulting education
environmental govtpp healthcaremed intlang law nonprofitsocialservices
profservicesbus salesmarketing  ownbusiness mileslt250 reunion soc
affinity acad devug arts campus sports otheract socsci natsci art
nongrad loans_z grants_z  finaid_miss djia_pctch campaignyr hock_pc rank
hock_pc_male hock_pc_yrsgrad hock_pc_sports rank_male rank_yrsgrad if
stillug==0, i(pidm)

xttobit lnreal1 male yrsgrad yrsgradsq mar alumnicloserelative
med_income artsent bankfin commedia comptech consulting education
environmental govtpp healthcaremed intlang law nonprofitsocialservices
profservicesbus salesmarketing  ownbusiness mileslt250 reunion soc
affinity acad devug arts campus sports otheract socsci natsci art
nongrad loans_z grants_z  finaid_miss djia_pctch campaignyr hock_pc rank
hock_pc_male hock_pc_yrsgrad hock_pc_sports rank_male rank_yrsgrad if
stillug==0, ll(1) i(pidm)


I have been asked by a referee to

1) test for hetereoskedasticity and correct if necessary

and

2) test for serial correlaton and correct if necessary.

Stata does not seem to have any commands to detect and/or correct for
hetereoskedasticity and serial correlation for xtprobit or xttobit. Any
suggestions would be most appreciated...

Thanks,

Jessica
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