[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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

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

Subject |
RE: st: 3 Problems in Panel Data Analysis |

Date |
Tue, 14 Oct 2008 17:08:30 +0100 |

Fardad et al., A small addendum to what Nils and Martin have said, specifically regarding the Hausman test of fixed vs. random effects: the standard form of this test is not valid in the presence of heteroskedasticity or serial correlation. A test that is valid in the presence of these problems is implemented by -xtoverid-, downloadable from ssc archives in the usual way. Why is it implemented in an overid program? Because the Hausman test in this case is a form of overid test. The fixed effects estimator assumes only that the regressors are orthogonal to the idiosyncratic error term e_it. The random effects estimator uses more orthogonality conditions, namely that the regressors are also orthogonal to the group-specific error term u_i. These extra orthogonality conditions are overidentifying restrictions, and as such they can be tested. See -help xtoverid- and the references therein. HTH, Mark Prof. Mark Schaffer 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 > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Nils Braakmann > Sent: 14 October 2008 14:38 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: 3 Problems in Panel Data Analysis > > Hi Fardad, > > no problem. Replys below > > > > 1- concerning point 1: do you know an other test in place of Hausman > > test? Is there any formal way to test for the conditions of RE (i.e. > > correlation between unobserved heterogeneity and the variables of > > interest)? How can tell something justifiable about this > correlation? > > Only based on theoretical arguments or is there any test whatsoever > > for this purpose? > > Well, as far as I know the standard approach for choosing between FE > and RE is the Hausman test. Looking at the correlation between the > individual fixed effects and the explanatory variables might work but > I would be sceptical: As long as the time dimension of your data is > not large (that is you have both a large number of firms and a large > number of observations for each firm where "large" refers to the > ususal "to infinity" asymptotics) your firm effects will be poorly > estimated and their correlation with any other variable would not be > particularly meaningful. As a more general point: I would generally > assume that there is unoberserved heterogeneity as long as I don't > have an unusually rich data set or have compelling evidence (e.g. > experimental data) that suggests the opposite. > > > As an alternative you suggest using IV. But you suggest that the IV > > should not be correltated with the outcome. I think you meant the > > other way around. Right? Nevertheless, in my case there is no > > meaningful, relevant IV available; so, this approach is out of > > question. > > Nope, the usual setup for an IV-estimate is that the instrument is > correlated with the outcome only through its correlation with the > (instrumented) variable of interest (see e.g. Cameron, A. Colin and > Prvain K. Trivedi, 2005 "Microeconometrics - Methods and > Applications", Cambridge University Press, pp. 96-98). However, this > does not seem to be a solution in your case. > > > 2- to be honest, I didn't exactly get your point. Sorry for > my limited > > econometric knowledge. What I know is that if my error terms are > > heteroskedasticit, then the estimates will be biased. As a remedy, > > robust coefficients should be estimated. Is there any other way to > > deal with the problem? Could you explain what you meant in your > > answer? > > First, as Martin already pointed out: Heteroscedasticity does bias the > estimates of the stadard errors but not the coefficients. Second, as > you have panel data you have an additional (and usually worse) > problem: Your error terms will be correlated within firms across time. > Using clustered standard errors corrects for arbitrary forms of > heteroscedasticity and autocorrelation within clusters (=firms in your > example). > > > My specific problem is that -xttobit- in contrast to -xtreg- doesn't > > have any robust options in Stata. How would you recommend > me to reduce > > the unwanted effects of heteroskedasticity? > > Puh, never used -xttobit-. You might want to try bootstrapped standard > errors but resample clusters of obervations (=firms with all > obervations for that firm) instead of obervations > (firm-year-obervations that is). I am not sure if this works with > -xttobit- though. > > > 3- You refer to RHS variables in your answer. Do you mean > variables of > > interest in the set of explanatory variables? With respect to your > > suggestion, do you think SYS-GMM will resolve the problems of both > > simultaneity and unobserved heterogeneity in my sample? > > By RHS(=right hand side) variables I referred to all explanatory > variables. System GMM should in principle help but you should refer to > the two papers by Roodman first as it is easy to do something stupid > with this estimator. You could also use -xtivreg- or its extension > -xtivreg2- by Mark Schaffer Baum, Schaffer with first differences and > additonal lags as instruments. > > > what are the commands to use first-difference and lagged independent > > variables at the same time in Stata, if any? > > -xtivreg- and -xtivreg2- by Mark Schaffer for example. > > > To be specific, how would compare xtabond, xtabond2 and > xtdpdsys with > > each others? Which one would you compare? What are the required > > conditions to be able to safely use these methods? > > Well, the last time I used a dynamic panel estimator, I was using > Version 9.2 which only had -xtabond- and -xtabond2- as an ado-file. I > am neither sure about the capabilities of the -xtabond-command in > Version 10.1 nor did I ever look at -xtdpdsys- in detail. A rather > detailed and accessible exposition of the necessary assumption for > Arelano-Bond/System GMM can be found in the papers by Roodman. > > > 4- As to my still remaining question, in a panel data setting, what > > pre- and post-tests do you recommend in general to check for the > > underling conditions and assumptions? What can one do to > increase the > > reliability and validly of the results? > > Well, for the standard RE estimator the crucial assumption is that the > explanatory variables are both uncorrelated with the unobserved > heterogeneity and the contemporaneous error. The standard FE estimator > allows for correlation between the unobserved heterogeneity and the > explanatory variables but still requires the latter to be uncorrelated > with the contemporaneous error. As all assumptions refer to > unobservables they are obviously hard to test... In fact, I am simply > not aware of any formal test though there might be one. > > Best regards, > Nils > * > * 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: 3 Problems in Panel Data Analysis***From:*"Fardad Zand" <fardad.zand@gmail.com>

**Re: st: 3 Problems in Panel Data Analysis***From:*"Nils Braakmann" <nilsbraakmann@googlemail.com>

**Re: st: 3 Problems in Panel Data Analysis***From:*"Fardad Zand" <fardad.zand@gmail.com>

**Re: st: 3 Problems in Panel Data Analysis***From:*"Nils Braakmann" <nilsbraakmann@googlemail.com>

- Prev by Date:
**Re: st: RE: rowskew?** - Next by Date:
**st: weights panel-survey data** - Previous by thread:
**Re: st: 3 Problems in Panel Data Analysis** - Next by thread:
**Re: st: RE: 3 Problems in Panel Data Analysis** - Index(es):

© Copyright 1996–2017 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |