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

From |
Steven Archambault <archstevej@gmail.com> |

To |
statalist@hsphsun2.harvard.edu, ">" <M.E.Schaffer@hw.ac.uk> |

Subject |
Re: st: RE: Hausman test for clustered random vs. fixed effects (again) |

Date |
Thu, 2 Jul 2009 15:40:51 -0600 |

Mark, I should have commented on this earlier, but when I eye the coefficients for both the FE and RE results, I see that some of them are quite different from one another. However, the xtoverid result suggests RE is the one to use. Does anybody see this as a problem? The numerator of the Hausman wald test is the difference in coefficients of the two models. Is this not missed in the xtoverid approach? I am posting my regression results to show what I am talking about more clearly. Thanks for your input. -Steve Fixed-effects (within) regression Number of obs = 404 Group variable: id_code_id Number of groups = 88 R-sq: within = 0.2304 Obs per group: min = 1 between = 0.4730 avg = 4.6 overall = 0.4487 max = 7 F(9,87) = 2.47 corr(u_i, Xb) = -0.9558 Prob > F = 0.0148 (Std. Err. adjusted for 88 clusters in id_code_id) ------------------------------------------------------------------------------ | Robust lnfd | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lags | -.0267991 .0185982 -1.44 0.153 -.063765 .0101668 lagk | .0964571 .0353269 2.73 0.008 .0262411 .166673 lagp | .2210296 .1206562 1.83 0.070 -.0187875 .4608468 lagdr | -.0000267 .0000251 -1.06 0.291 -.0000767 .0000232 laglurb | .3483909 .1234674 2.82 0.006 .102986 .5937957 lagtra | .1109513 .1267749 0.88 0.384 -.1410275 .3629301 lagte | .0067764 .004166 1.63 0.107 -.0015039 .0150567 lagcr | .0950221 .0683074 1.39 0.168 -.0407463 .2307905 lagp | .0343752 .1291378 0.27 0.791 -.2223001 .2910506 _cons | 4.316618 1.996618 2.16 0.033 .348124 8.285112 -------------+---------------------------------------------------------------- sigma_u | .44721909 sigma_e | .0595116 rho | .98260039 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Random-effects GLS regression Number of obs = 404 Group variable: id_code_id Number of groups = 88 R-sq: within = 0.1792 Obs per group: min = 1 between = 0.5074 avg = 4.6 overall = 0.5017 max = 7 Random effects u_i ~ Gaussian Wald chi2(9) = 48.97 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 (Std. Err. adjusted for clustering on id_code_id) ------------------------------------------------------------------------------ | Robust lnfd | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lags | -.01138 .0135958 -0.84 0.403 -.0380274 .0152673 lagk | .0115314 .0180641 0.64 0.523 -.0238735 .0469363 lagp | .2551701 .119322 2.14 0.032 .0213033 .4890369 lagdr | -6.17e-06 .0000153 -0.40 0.686 -.0000361 .0000238 laglurb | .0657802 .0153923 4.27 0.000 .0356119 .0959486 lagtra | .0022183 .0579203 0.04 0.969 -.1113034 .11574 lagte | .0048012 .0016128 2.98 0.003 .00164 .0079623 lagcr | .1051833 .045994 2.29 0.022 .0150368 .1953298 lagp | .184373 .1191063 1.55 0.122 -.0490711 .4178171 _cons | 9.071133 .2322309 39.06 0.000 8.615968 9.526297 -------------+---------------------------------------------------------------- sigma_u | .10617991 sigma_e | .0595116 rho | .76095591 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . xtoverid; Test of overidentifying restrictions: fixed vs random effects Cross-section time-series model: xtreg re robust Sargan-Hansen statistic 14.684 Chi-sq(9) P-value = 0.1000 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/ > * * 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/

**Follow-Ups**:**RE: st: RE: Hausman test for clustered random vs. fixed effects (again)***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

- Prev by Date:
**st: Quintiles and crosstables with multiple imputed data** - Next by Date:
**st: Re: Stata 11** - Previous by thread:
**st: Quintiles and crosstables with multiple imputed data** - Next by thread:
**RE: st: RE: Hausman test for clustered random vs. fixed effects (again)** - Index(es):

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