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


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-)
>
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>
>
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