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From |
"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: RE: Instrumental variables and panel data |

Date |
Mon, 12 Oct 2009 23:56:27 +0100 |

Jaime, > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Jaime Gómez > Sent: 12 October 2009 23:34 > To: statalist@hsphsun2.harvard.edu > Subject: st: RE: RE: Instrumental variables and panel data > > Dear Mark > > Thank you very much for your message. The problem is that > (with xtoverid) I do not know any way to ascertain whether > the possibly endogenous variable is exogenous or whether I > have a weak instruments problem (or whether the random > effects estimates are preferred over the fixed). With > xtoverid, is there any way to know the estimates I have to > rely on?. The discussion on Statalist about -xtoverid- that I mentioned included a discussion of how to hack the code to make it do things like endogeneity tests. I think that Austin Nichols even provided a link to a downloadable -xtoverid2- that would do this. -xtoverid- calls -ivreg2- internally, and you can see from the discussion how to hack the internal call to -ivreg2- to do what you want it to do. Happy hacking! Cheers, Mark > In fact, using the > ivreg2 command with the endog( ) option shows that the > variable is not endogenous, but this is not a panel data > estimation and I do not know whether, from the ivreg2 > estimation, I can simply conclude that there is not an > endogeneity problem. In any case, I still would have to solve > the problem of getting the coefficients of the time-invariant > dummies if the Hausman test indicates that the fixed effects > is the preferred estimation (could xthaylor provide a > consistent solution?). > > On the other hand, I have been suggested to estimate GMM > System through xtabond2, but reading David Roodman's paper, > it seems to me that the context in which this is applied is > different (1. I have dummy variables that could bias the > results; 2. I have 59 firms followed an average of 25 > quarterly periods; 3. I have a good external instrument; 4. I > do not have lags of dependent variables as regressors). > Please, any advise on this? > > Thanks ! > > Jaime. > > > > > -----Mensaje original----- > De: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de > Schaffer, Mark E Enviado el: jueves, 08 de octubre de 2009 16:22 > Para: statalist@hsphsun2.harvard.edu > Asunto: st: RE: Instrumental variables and panel data > > Jaime, > > > -----Original Message----- > > From: owner-statalist@hsphsun2.harvard.edu > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Jaime Gómez > > Sent: 06 October 2009 23:13 > > To: statalist@hsphsun2.harvard.edu > > Subject: st: Instrumental variables and panel data > > > > Dear Statalisters > > > > We have a model in which firm performance depends on (1) > the order of > > entry and (2) a possibly endogenous variable and (3) other > variables, > > including time dummies. First, we were suggested to use > instrumental > > variable techniques and to provide HAC standard errors, > something we > > have already done with the ivreg2 command in Stata and using an > > external instrument. We tested for the exogeneity of the possibly > > endogenous variable through the endog( ) option and the test shows > > that the variable could be considered exogenous. > > > > In a second step, we have been suggested to use the panel > structure of > > our data and, simultaneously, to consider the endogeneity problem. > > Ideally, we would like (1) to estimate a panel data model with > > instrumental variables and HAC errors, > > (2) to test for the exogeneity of our possible endogenous > variable and > > (3) to check whether the fixed or random effects model is > appropriate. > > So, it seems that the xtivreg or > > xtivreg2 commands could be the solution. Nevertheless, we > have several > > problems: > > > > 1) the order of entry is represented through time invariant dummies > > (pioneer, second mover, third mover, ...) that drop when we > estimate a > > fixed effects model, but we are (very) interested in the > values of the > > coefficients. So it seems that the only way of getting these > > coefficients is to estimate a random effects model and > check whether > > this is appropriate with a Hausman test (If I reject the random > > effects model, ¿could I get the order of entry coefficients through > > another panel data technique?) > > > > 2) Before doing so we have to find the way of getting HAC standard > > errors. I think I would know how to do this with > > xtivreg2 (I am assuming that the options are similar to the ones in > > ivreg2), nevertheless it seems that there is no way of estimating a > > random effects model with xtivreg2. The problem with using xtivreg > > seems that the estimation and postestimation options are much more > > restricted than with > > xtivreg2 (for example, how do I get HAC errors? How do I > test for the > > endogeneity of the regressor? Should I use xtoverid for testing for > > the appropriateness of the random effects model?). > > > > In summary, is there any way for treating all these issues > (possibly > > omitted variables that advise the use of panel data > techniques, time > > invariant variables of interest, HAC standard errors and > instrumental > > variables) at the same time? > > Alternatively, could you suggest another strategy to tackle all the > > problems with Stata (perhaps sequentially?)? > > A couple of thoughts... > > 1. You can use -xtoverid- with the undocumented -noisily- > option to estimate a random effects model with various types > of robust SEs. There have been several threads on Statalist > about it, so it should be pretty easy to find. (I really > have to get around to making -xtivreg2- do random > effects....) > > 2. Cluster-robust SEs are robust to arbitrary within-cluster > correlation as well as heteroskedasticity, and you can think > of them as a variety of HAC SEs. The main difference between > them and the usual kernel-based HAC SEs (as supported by > -xtivreg2- et al.) is that the asymptotics for cluster-robust > SEs have the number of clusters going off to infinity; the > asymptotics for the usual kernel HAC SEs (Bartlett kernel aka > Newey-West and all those guys) is that they require time to > go off to infinity. Most panels these days are > small-T-large-N, so chances are you would be better off with > cluster-robust. Of course, it's up to you. > > Cheers, > Mark > > > Thanks a lot > > Sincerely > > Jaime Gómez > > Universidad de Zaragoza > > > > > > * > > * 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/ > -- 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/

**References**:**st: Instrumental variables and panel data***From:*Jaime Gómez <jaime.gomez@unizar.es>

**st: RE: Instrumental variables and panel data***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

**st: RE: RE: Instrumental variables and panel data***From:*Jaime Gómez <jaime.gomez@unizar.es>

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