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From |
"Arne Risa Hole" <arnehole@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sat, 7 Jul 2007 11:24:22 +0100 |

In my rush yesterday I forgot to mention Richard Williams' -oglm- module which can also be used to estimate heteroscedastic logit and probit models with cluster-robust SEs. This is an easier option for estimating the heteroscedastic binary logit than -clogithet- since you won't have to reshape your data. Arne On 06/07/07, Arne Risa Hole <arnehole@gmail.com> wrote:

Thanks Austin for mentioning my -clogithet- module and paper. I'm afraid, however, that -clogithet- does not estimate a fixed effects logit with heteroscedasticity but McFadden's choice model with het - although Stata's -clogit- command can estimate homoscedastic versions of both of these -clogithet- only does the latter unfortunately. What you could do is estimate a hetersocedastic binary logit model with het using -clogithet- with cluster-robust standard errors, along the lines of AttaUllah Shah's suggestion. You can also use -hetprob- in this manner if you prefer a probit model. Arne On 06/07/07, Austin Nichols <austinnichols@gmail.com> wrote: > 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 > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ >

* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?***From:*"Holmes, Jessica" <jholmes@middlebury.edu>

**References**:**st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?***From:*"Holmes, Jessica" <jholmes@middlebury.edu>

**Re: st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?***From:*"Austin Nichols" <austinnichols@gmail.com>

**Re: st: hetereoskedasticity/serial correlation with RE xtprobit xttobit?***From:*"Arne Risa Hole" <arnehole@gmail.com>

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