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From |
Marco Buur <marco.buur@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust |

Date |
Wed, 19 Aug 2009 12:22:28 +0200 |

Dear All I am wondering if xttest3 checks heteroskedasticity in fixed effect regression model between the clusters or it is better to use xtcltest or cltest. I tiried to run xtcltest or cltest (Stata) 10 it didn't work. Any hint? xtreg , fe xttest3 Marco On Tue, Aug 18, 2009 at 5:28 PM, Steven Archambault<archstevej@gmail.com> wrote: > On Tue, Aug 18, 2009 at 9:27 AM, Steven Archambault<archstevej@gmail.com> wrote: >> This approach is working. I was hoping to calculate the Cragg Donald >> Wald by hand, but it seems I cannot get the "minimum eigenvalue of >> the Cragg-Donald" statistic from ereturn. Would this be something I >> could calculate another way myself? >> >> >> >> >> >> >> On Thu, Aug 13, 2009 at 4:57 PM, Schaffer, Mark E<M.E.Schaffer@hw.ac.uk> wrote: >>> Steve, >>> >>>> -----Original Message----- >>>> From: Steven Archambault [mailto:archstevej@gmail.com] >>>> Sent: 13 August 2009 23:48 >>>> To: statalist@hsphsun2.harvard.edu >>>> Cc: austinnichols@gmail.com; Alfred.Stiglbauer@oenb.at; >>>> Schaffer, Mark E >>>> Subject: Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust >>>> >>>> Is there a way to analyze instrumented panel data using >>>> random effects and robust standard errors? It seems the >>>> current programs don't allows this. >>> >>> You can used -xtoverid- to do this. To get an overid stat after -xtivreg- with random effects, -xtoverid- reestimates everything internally, and if you ask for a robust overid stat, that means it reestimates internally with robust SEs. >>> >>> If you add the option -noi- (for "noisily") to -xtoverid- after your estimation, you can see the results of the internal reestimation of the random effects model. >>> >>> The only problem is ... the variable names in the -xtoverid- output will all be Stata internal macros with names like __0000001 and so forth. You can tell which is which by matching the values of the coefficients in the -xtoverid- output to the values in the output from your original estimation. A bit of a hassle but it should work. >>> >>> Hope this helps. >>> >>> Cheers, >>> Mark >>> >>>> On Wed, Aug 12, 2009 at 10:28 AM, Steven >>>> Archambault<archstevej@gmail.com> wrote: >>>> > Mark, >>>> > >>>> > Many thanks for your response, this clears up several >>>> questions. Yes, >>>> > I meant having a chi sq value that accepts the null that >>>> there is no >>>> > difference between RE and FE coefficients, implying the >>>> efficient RE >>>> > model is preferred. >>>> > >>>> > -Steve >>>> > >>>> >> On Wed, Aug 12, 2009 at 6:44 AM, Schaffer, Mark E >>>> <M.E.Schaffer@hw.ac.uk> wrote: >>>> >>> >>>> >>> Steve, >>>> >>> >>>> >>> I'm not sure exactly what you mean in your question. For >>>> one thing, >>>> >>> rejection of the null means rejection of RE in favour of FE. But >>>> >>> assuming that's just a typo, here's an attempt at a >>>> restatement of >>>> >>> the question and an answer: >>>> >>> >>>> >>> 1. The difference between FE and RE can be stated in GMM >>>> terms (see >>>> >>> Hayashi's "Econometrics" for a good exposition). The FE >>>> estimator >>>> >>> uses only the orthogonality conditions that say the demeaned >>>> >>> regressor X is orthogonal to the idiosyncratic term e_ij. The RE >>>> >>> estimator uses these orthogonality conditions, plus the >>>> >>> orthogonality conditions that say that the mean of X for >>>> the panel >>>> >>> unit is orthogonaly to the panel error term u_j. >>>> >>> >>>> >>> 2. This is why the FE vs RE test is an overid test. The RE >>>> >>> estimator uses more orthogonality conditions, and so the >>>> equation is >>>> >>> overidentified. In the special case of classical iid errors, the >>>> >>> Hausman test is numerically the same as the Sargan-Hansen test. >>>> >>> >>>> >>> 3. Your question is, what happens if some of the Xs are >>>> endogenous >>>> >>> and you have some Zs as instruments? The answer is that the same >>>> >>> GMM framework encompasses this. You remove some of the >>>> demeaned Xs >>>> >>> from the orthogonality conditions and add some demeaned Zs to the >>>> >>> orthogonality conditions, and if you are using an RE >>>> estimator, you >>>> >>> also remove the panel unit means of the Xs from the orthogonality >>>> >>> conditions and add some panel unit means of Zs to them. (This is >>>> >>> the case for the EC2SLS RE estimator - it's a bit >>>> different for the >>>> >>> G2SLS estimator. The reason is that the G2SLS using a single >>>> >>> quasi-demeaned instrument Z instead of the demeaned Z and >>>> panel unit >>>> >>> mean Z separately, which is what EC2SLS does. I think >>>> the intuition >>>> >>> for EC2SLS is easier to get.) >>>> >>> >>>> >>> 4. If the FE model is overidentified, you'll now have an overid >>>> >>> test stat for it that tests the validity of the demeaned >>>> Zs as instruments. >>>> >>> If you're estimating an RE model, the overid test will test the >>>> >>> validity of the demeaned and panel unit means of the Zs >>>> and also the >>>> >>> panel unit means of the exogenous Xs. >>>> >>> >>>> >>> 5. If the overid test with endogenous regressors rejects the RE >>>> >>> model, you have a standard GMM problem: which of your >>>> orthogonality >>>> >>> conditions is invalid? It could be the demeaned Zs, or the panel >>>> >>> unit means of the Xs, or both, or whatever. In that >>>> case, you can >>>> >>> do a "GMM distance test" (aka "C test", >>>> "Difference-in-Sargan test", >>>> >>> etc.) where you compare the Sargan-Hansen test stat (from >>>> >>> -xtoverid-) after estimation with and without the orthognality >>>> >>> conditions that you think are the likely culprits. But >>>> you have to >>>> >>> decide ex ante which are the dubious ones - econometric >>>> theory can't tell you. >>>> >>> >>>> >>> Hope this helps. >>>> >>> >>>> >>> Yours, >>>> >>> Mark >>>> >>> >>>> >>> Prof. Mark Schaffer FRSE >>>> >>> Director, CERT >>>> >>> Department of Economics >>>> >>> School of Management & Languages >>>> >>> Heriot-Watt University, Edinburgh EH14 4AS tel +44-131-451-3494 / >>>> >>> fax +44-131-451-3296 http://ideas.repec.org/e/psc51.html >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> ________________________________ >>>> >>> >>>> >>> From: Steven Archambault [mailto:archstevej@gmail.com] >>>> >>> Sent: 12 August 2009 08:50 >>>> >>> To: statalist@hsphsun2.harvard.edu; Schaffer, Mark E >>>> >>> Cc: austinnichols@gmail.com; Alfred.Stiglbauer@oenb.at >>>> >>> Subject: Sargen-Hansen and instruments--RE vs. FE >>>> >>> >>>> >>> >>>> >>> A while back we discussed the use of the >>>> Sargen-Hansen test >>>> >>> to check if RE was an appropriate analysis to use for >>>> panel data. My >>>> >>> question now is regarding suspected endogeneity problems. If the >>>> >>> Sargen-Hansen statistic is such that you reject the null, >>>> in favor >>>> >>> of using the RE, does it follow that we do not need to >>>> worry about >>>> >>> explanatory variables being endogenous? My feeling is >>>> yes, here is >>>> >>> the logic. If I were to use xtivreg I would call the same over >>>> >>> identification test to see if my instruments are valid. >>>> So, if the >>>> >>> test already rejects before adding instruments, I should not need >>>> >>> the instruments. >>>> >>> >>>> >>> If I do use instruments, what is then a valid test to >>>> >>> determine if RE is an appropriate model to use (over FE)? >>>> >>> >>>> >>> Is my question clear? >>>> >>> >>>> >>> Thanks, >>>> >>> Steve >>>> >>> >>>> >>> >>>> >>> >>>> >>> On Sat, Jun 27, 2009 at 11:31 AM, Schaffer, Mark E >>>> >>> <M.E.Schaffer@hw.ac.uk> wrote: >>>> >>> >>>> >>> >>>> >>> Steve, >>>> >>> >>>> >>> > -----Original Message----- >>>> >>> > From: owner-statalist@hsphsun2.harvard.edu >>>> >>> > [mailto:owner-statalist@hsphsun2.harvard.edu] On >>>> >>> Behalf Of >>>> >>> > Steven Archambault >>>> >>> > Sent: 27 June 2009 00:26 >>>> >>> > To: statalist@hsphsun2.harvard.edu; >>>> >>> austinnichols@gmail.com; >>>> >>> > Alfred.Stiglbauer@oenb.at >>>> >>> > Subject: st: Hausman test for clustered >>>> random vs. >>>> >>> fixed >>>> >>> > effects (again) >>>> >>> > >>>> >>> > Hi all, >>>> >>> > >>>> >>> > I know this has been discussed before, >>>> but in STATA >>>> >>> 10 (and >>>> >>> > versions before 9 I understand) the canned >>>> >>> procedure for >>>> >>> > Hausman test when comparing FE and RE >>>> models cannot >>>> >>> be run >>>> >>> > when the data analysis uses clustering (and by >>>> >>> default >>>> >>> > corrects for robust errors in STATA 10). >>>> >>> > This is the error received >>>> >>> > >>>> >>> > "hausman cannot be used with vce(robust), >>>> >>> vce(cluster cvar), >>>> >>> > or p-weighted data" >>>> >>> > >>>> >>> > My question is whether or not the >>>> approach of using >>>> >>> xtoverid >>>> >>> > to compare FE and RE models (analyzed using the >>>> >>> clustered and >>>> >>> > by default robust approach in STATA 10) >>>> is accepted >>>> >>> in the >>>> >>> > literature. This approach produces the >>>> >>> Sargan-Hansen stat, >>>> >>> > which is typically used with analyses that have >>>> >>> > instrumentalized variables and need an >>>> >>> overidentification >>>> >>> > test. For the sake of publishing I am >>>> wondering if >>>> >>> it is >>>> >>> > better just not to worry about >>>> heteroskedaticity, >>>> >>> and avoid >>>> >>> > clustering in the first place (even though >>>> >>> heteroskedaticity >>>> >>> > likely exists)? Or, alternatively one could just >>>> >>> calculate >>>> >>> > the Hausman test by hand following the clustered >>>> >>> analyses. >>>> >>> > >>>> >>> > Thanks for your insight. >>>> >>> >>>> >>> It's very much accepted in the literature. In the >>>> >>> -xtoverid- help file, >>>> >>> see especially the paper by Arellano and >>>> the book by >>>> >>> Hayashi. >>>> >>> >>>> >>> If you suspect heteroskedasticity or clustered >>>> >>> errors, there really is >>>> >>> no good reason to go with a test (classic Hausman) >>>> >>> that is invalid in >>>> >>> the presence of these problems. The GMM >>>> -xtoverid- >>>> >>> approach is a >>>> >>> generalization of the Hausman test, in the >>>> following >>>> >>> sense: >>>> >>> >>>> >>> - The Hausman and GMM tests of fixed vs. random >>>> >>> effects have the same >>>> >>> degrees of freedom. This means the result >>>> cited by >>>> >>> Hayashi (and due to >>>> >>> Newey, if I recall) kicks in, namely... >>>> >>> >>>> >>> - Under the assumption of homoskedasticity and >>>> >>> independent errors, the >>>> >>> Hausman and GMM test statistics are numerically >>>> >>> identical. Same test. >>>> >>> >>>> >>> - When you loosen the iid assumption and allow >>>> >>> heteroskedasticity or >>>> >>> dependent data, the robust GMM test is the natural >>>> >>> generalization. >>>> >>> >>>> >>> Hope this helps. >>>> >>> >>>> >>> Cheers, >>>> >>> Mark (author of -xtoverid-) >>>> >>> >>>> >>> > * >>>> >>> > * For searches and help try: >>>> >>> > * http://www.stata.com/help.cgi?search >>>> >>> > * http://www.stata.com/support/statalist/faq >>>> >>> > * http://www.ats.ucla.edu/stat/stata/ >>>> >>> > >>>> >>> >>>> >>> >>>> >>> -- >>>> >>> Heriot-Watt University is a Scottish charity >>>> >>> registered under charity number SC000278. >>>> >>> >>>> >>> >>>> >>> * >>>> >>> * For searches and help try: >>>> >>> * http://www.stata.com/help.cgi?search >>>> >>> * http://www.stata.com/support/statalist/faq >>>> >>> * http://www.ats.ucla.edu/stat/stata/ >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> -- >>>> >>> Heriot-Watt University is a Scottish charity registered under >>>> >>> charity number SC000278. >>>> >>> >>>> >>> >>>> >>> * >>>> >>> * For searches and help try: >>>> >>> * http://www.stata.com/help.cgi?search >>>> >>> * http://www.stata.com/support/statalist/faq >>>> >>> * http://www.ats.ucla.edu/stat/stata/ >>>> >> >>>> > >>>> >>> >>> >>> -- >>> Heriot-Watt University is a Scottish charity >>> registered under charity number SC000278. >>> >>> >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust***From:*Steven Archambault <archstevej@gmail.com>

**RE: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

**Re: st: RE: Sargen-Hansen and instruments--RE vs. FE--Robust***From:*Steven Archambault <archstevej@gmail.com>

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