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THANKS - was Re: st: -xtreg, re- vs -regress, cluster ()-


From   Enrica Croda <croda@nicco.sscnet.ucla.edu>
To   statalist@hsphsun2.harvard.edu
Subject   THANKS - was Re: st: -xtreg, re- vs -regress, cluster ()-
Date   Mon, 9 Dec 2002 20:27:49 -0800 (PST)

Many thanks to Marcela Perticara and to Mark Schaffer for their very
helpful replies.

It seems indeed that Stata sets sigma_u to zero because it finds a
negative value for it. (At least,  I tried to calculate sigma_u
"manually" following the formula Marcela refers to below, and I got a
negative values).

Thanks a lot again,

Enrica





On Thu, 5 Dec 2002, Marcela Perticara wrote:

> In the RE model the best quadratic unbiased estimators of the variance
> components come directly from the spectral decomp. of the covariance matrix
> of the model error. You don't get a direct estimate for sigma_u, but an
> estimate for sigma_e and an estimate for sigma_1=T*sigma_u+sigma_e. Then
> sigma_u is obtained as sigma_u=(sigma_1-sigma_e)/T
> Stata implements two different methods to estimates these variances
> components, but both of them estimate sigma_u using this last formula and
> there is no guarantee that this estimator will be greater than zero.
> Stata replaces sigma_u for zero, whenever it finds a negative estimate. That
> is, at the end Stata computes
> sigma_u=max{0,(sigma_1-sigma_e)/T} [I don't have the manuals with me but I
> am sure this is clearly specified in the reference manual]. And of course
> when sigma_u is set to be zero, BetaGLS is reduced to BetaOLS.
> There is another estimate for sigma_u that you can use. You estimate sigma_u
> using the sample variance of the individual fixed effects obtained from a
> dummy variable LS regression.
>
>
>
> --------------------------------------------
> Universidad Alberto Hurtado
> ILADES / Georgetown University
> Erasmo Escala 1835
> Santiago, Chile
> Phono: 671-7130 anexo 267
> http://www.ilades.cl/economia/index.html
>
> ----- Original Message -----
> From: "Mark Schaffer" <M.E.Schaffer@hw.ac.uk>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Thursday, December 05, 2002 11:35 AM
> Subject: Re: st: -xtreg, re- vs -regress, cluster ()-
>
>
> > Enrica,
> >
> > Date sent:      Thu, 5 Dec 2002 02:23:47 -0800 (PST)
> > From:           Enrica Croda <croda@nicco.sscnet.ucla.edu>
> > To:             statalist@hsphsun2.harvard.edu
> > Subject:        st: -xtreg, re-  vs  -regress, cluster ()-
> > Send reply to:  statalist@hsphsun2.harvard.edu
> >
> > > Hello Stata-listers:
> > >
> > > I am a bit puzzled by some regression results I obtained using -xtreg,
> re-
> > > and -regress, cluster()- on the same sample.
> > >
> > > I would appreciate if anybody out there could give me feedback on
> whether
> > > it possible to obtain the same coefficient estimated by using -regress,
> > > cluster(ID)- and -xtreg, re i(ID)- on the same specification on
> > > the same sample, and if there are common circumstances in which this may
> > > happen.
> >
> > This will happen only in "degenerate" cases.
> >
> > -regress- with -cluster- gives you the same coefficients as regress,
> > but with standard errors that are robust to intra-group correlation
> > (in your case, correlation between observations of the same married
> > woman at different points in time).
> >
> > -xtreg, re- gives you estimates for the "random effects" model.  This
> > is a different specification, and you'll normally get different
> > coefficients.
> >
> > The issue is "normally".  You have, in effect, a collinearity
> > problem.  What is happening is that the random effects model is
> > reducing to standard OLS.  You can tell by the following lines at the
> > bottom of the -xtreg,re- output:
> >
> >      sigma_u |          0
> >      sigma_e |  .28993302
> >          rho |          0   (fraction of variance due to u_i)
> >
> > u_i is the "random effect", and this output is basically telling you
> > that it has no role in what you've estimated.  The results are OLS.
> >
> > This is why the MLE results are different - you'll see that the
> > sigma_u for that estimation is not zero, and you are getting what you
> > expected (ie, not OLS).
> >
> > I don't remember offhand all the circumstances that can cause this to
> > happen with the random effects estimator, but that is what is going
> > on.
> >
> > Hope this helps.
> >
> > --Mark
> >
> > NB: I've seen this come up on the list before.  Does anyone else
> > think that -xtreg,re- should print a warning when random effects
> > degenerates into OLS?
> >
> > >
> > > As far as the specifics of my case, I am studying labor force
> > > participation of married women.
> > > I am using a balanced panel data-set in "long form" (iis: ID, tis year)
> > > containing yearly data for the period 1990-1997.
> > > I have a total of 8696 observations on 1087 married women.
> > >
> > > The dependent variable is a binary variable with values 1 or 0.
> > >
> > > I run
> > > 1) pooled OLS regressions with the cluster option (-regress,
> cluster(ID)-,
> > > and
> > > 2) -xtreg, re i(ID)-
> > > on the same specification.
> > >
> > > If I use a static specification and do not include any lagged variable
> > > among the explanatory variables, applying the 2 different estimation
> methods
> > > produces different coefficient estimates and different standard errors.
> > > And this is what I was expecting.
> > >
> > > What is puzzling me is the following.
> > >
> > > If I use a dynamic specification, i.e. basically I include the lagged
> > > value of the dependent variable among the explanatory variables,
> applying
> > > the two different estimation methods produces exactly the same
> > > coefficient estimates and different standard errors. (Estimation results
> > > follow)
> > > I was not expecting the coefficient estimates to be exactly the same
> with
> > > the two methods.
> > >
> > > I tried other panel regressions.
> > > -xtreg, mle- provides different estimates and standard errors
> from -xtreg,
> > > re-.
> > >
> > > I also tried to construct the random effects estimates by running a
> pooled
> > > regression on the quasi-differences specification (4) in Volume 4 of
> > > the Stata 7 Manual, p.437, with theta estimated as described on p. 452,
> > > and I got yet different results.
> > >
> > > I am reporting below the estimates obtained with
> > > I.  -regress, cluster(ID)-
> > > II. -xtreg, re i (ID)-
> > > III.-xtreg, mle i (ID)-
> > >
> > >
> > > Variable definition:
> > > curremplo: current employment status
> > > lagemplo : lagged employment status
> > > perminc  : husband's permanent income
> > > transinc : husband's transitory income
> > > age      : age/10
> > > agesq    : (age/10) squared
> > > sak02    : number of kids aged 0-2
> > > sak35    : number of kids aged 3-5
> > > sak02    : number of kids 6+
> > > east     : dummy variable =1 if respondent is East German (the data
> > >            are for East and West Germany)
> > > schoolmax: maximum years of schooling
> > > yr##     :year dummy, equal to 1 if year is ## (##=91,...97).
> > >
> > > ----------------------------------------------------------------------
> > > REGRESS, CLUSTER
> > >
> > > . regress curremplo perminc transinc sak02 sak35 sak6g lagemplo age
> agesq east
> > > > schoolmax yr91 yr92 yr93 yr94 yr95 yr96 yr97, cluster(persnr);
> > >
> > > Regression with robust standard errors                 Number of obs =
> 8696
> > >                                                        F( 17,  1086) =
> 411.72
> > >                                                        Prob > F      =
> 0.0000
> > >                                                        R-squared     =
> 0.5388
> > > Number of clusters (persnr) = 1087                     Root MSE      =
> .32573
> > >
> >
> > --------------------------------------------------------------------------
> ----
> > >              |               Robust
> > >    curremplo |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
> Interval]
> >
> > -------------+------------------------------------------------------------
> ----
> > >      perminc |   -.003359   .0016812    -2.00
> 0.046    -.0066579   -.0000602
> > >     transinc |  -.0029873   .0017223    -1.73   0.083    -.0063667
> .0003921
> > >        sak02 |  -.1735915   .0155283   -11.18
> 0.000    -.2040605   -.1431226
> > >        sak35 |  -.0343057   .0091977    -3.73
> 0.000    -.0523531   -.0162584
> > >        sak6g |  -.0222673   .0047493    -4.69
> 0.000    -.0315862   -.0129483
> > >     lagemplo |   .6713014    .012667    53.00   0.000     .6464469
> .6961559
> > >          age |    .010654   .0038414     2.77   0.006     .0031165
> .0181915
> > >        agesq |   -.000187    .000048    -3.89
> 0.000    -.0002813   -.0000927
> > >         east |   .0453875   .0097331     4.66   0.000     .0262897
> .0644853
> > >    schoolmax |   .0051449   .0018325     2.81   0.005     .0015493
> .0087405
> > >         yr91 |   -.031073   .0159144    -1.95   0.051    -.0622995
> .0001534
> > >         yr92 |  -.0133491   .0143174    -0.93   0.351     -.041442
> .0147438
> > >         yr93 |    -.02965     .01378    -2.15
> 0.032    -.0566885   -.0026115
> > >         yr94 |  -.0042043   .0134346    -0.31   0.754     -.030565
> .0221563
> > >         yr95 |   -.010533    .013451    -0.78   0.434    -.0369259
> .0158599
> > >         yr96 |  -.0319808   .0135433    -2.36
> 0.018    -.0585548   -.0054069
> > >         yr97 |  -.0140815   .0134361    -1.05   0.295    -.0404453
> .0122822
> > >        _cons |     .09109    .073401     1.24   0.215    -.0529337
> .2351137
> >
> > --------------------------------------------------------------------------
> ----
> > >
> > >
> > >
> > > XTREG, RE
> > >
> > > . xtreg curremplo perminc transinc sak02 sak35 sak6g lagemplo age agesq
> east
> > > >  schoolmax yr91 yr92 yr93 yr94 yr95 yr96 yr97, i(persnr) re;
> > >
> > > Random-effects GLS regression                   Number of obs      =
> 8696
> > > Group variable (i) : persnr                     Number of groups   =
> 1087
> > >
> > > R-sq:  within  = 0.0984                         Obs per group: min =
> 8
> > >        between = 0.9408                                        avg =
> 8.0
> > >        overall = 0.5388                                        max =
> 8
> > >
> > > Random effects u_i ~ Gaussian                   Wald chi2(17)      =
> 10137.10
> > > corr(u_i, X)       = 0 (assumed)                Prob > chi2        =
> 0.0000
> > >
> >
> > --------------------------------------------------------------------------
> ----
> > >    curremplo |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> >
> > -------------+------------------------------------------------------------
> ----
> > >      perminc |   -.003359   .0013716    -2.45
> 0.014    -.0060473   -.0006708
> > >     transinc |  -.0029873    .002286    -1.31   0.191    -.0074678
> .0014932
> > >        sak02 |  -.1735915   .0125305   -13.85
> 0.000    -.1981509   -.1490322
> > >        sak35 |  -.0343057   .0091113    -3.77
> 0.000    -.0521635   -.0164479
> > >        sak6g |  -.0222673   .0044685    -4.98
> 0.000    -.0310254   -.0135091
> > >     lagemplo |   .6713014   .0080104    83.80   0.000     .6556013
> .6870015
> > >          age |    .010654   .0038709     2.75   0.006     .0030672
> .0182408
> > >        agesq |   -.000187   .0000471    -3.97
> 0.000    -.0002792   -.0000947
> > >         east |   .0453875   .0087905     5.16   0.000     .0281584
> .0626166
> > >    schoolmax |   .0051449   .0016204     3.18   0.001     .0019691
> .0083208
> > >         yr91 |   -.031073   .0139985    -2.22
> 0.026    -.0585096   -.0036365
> > >         yr92 |  -.0133491   .0140428    -0.95   0.342    -.0408724
> .0141743
> > >         yr93 |    -.02965   .0140972    -2.10
> 0.035    -.0572799   -.0020201
> > >         yr94 |  -.0042043   .0141534    -0.30   0.766    -.0319445
> .0235358
> > >         yr95 |   -.010533   .0142409    -0.74   0.460    -.0384447
> .0173787
> > >         yr96 |  -.0319808   .0143176    -2.23
> 0.026    -.0600429   -.0039188
> > >         yr97 |  -.0140815   .0144083    -0.98   0.328    -.0423214
> .0141583
> > >        _cons |     .09109   .0777215     1.17   0.241    -.0612413
> .2434213
> >
> > -------------+------------------------------------------------------------
> ----
> > >      sigma_u |          0
> > >      sigma_e |  .28993302
> > >          rho |          0   (fraction of variance due to u_i)
> >
> > --------------------------------------------------------------------------
> ----
> > >
> > >
> > >
> > > XTREG, MLE
> > > . xtreg curremplo perminc transinc sak02 sak35 sak6g lagemplo age agesq
> east
> > > > schoolmax yr91 yr92 yr93 yr94 yr95 yr96 yr97, i(persnr) mle;
> > >
> > > Fitting constant-only model:
> > > Iteration 0:   log likelihood = -6568.6464
> > > Iteration 1:   log likelihood = -5790.8646
> > > Iteration 2:   log likelihood = -5653.5493
> > > Iteration 3:   log likelihood = -5646.3662
> > > Iteration 4:   log likelihood = -5646.3369
> > >
> > > Fitting full model:
> > > Iteration 0:   log likelihood = -2559.0813
> > > Iteration 1:   log likelihood = -2490.0659
> > > Iteration 2:   log likelihood = -2461.6401
> > > Iteration 3:   log likelihood = -2461.2976
> > > Iteration 4:   log likelihood = -2461.2973
> > >
> > > Random-effects ML regression                    Number of obs      =
> 8696
> > > Group variable (i) : persnr                     Number of groups   =
> 1087
> > >
> > > Random effects u_i ~ Gaussian                   Obs per group: min =
> 8
> > >                                                                avg =
> 8.0
> > >                                                                max =
> 8
> > >
> > >                                                 LR chi2(17)        =
> 6370.08
> > > Log likelihood  = -2461.2973                    Prob > chi2        =
> 0.0000
> > >
> >
> > --------------------------------------------------------------------------
> ----
> > >    curremplo |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> >
> > -------------+------------------------------------------------------------
> ----
> > >      perminc |  -.0056741   .0023379    -2.43
> 0.015    -.0102562   -.0010919
> > >     transinc |  -.0040303   .0020947    -1.92   0.054    -.0081358
> .0000752
> > >        sak02 |  -.2245123   .0133989   -16.76
> .000    -.2507737    -.198251
> > >        sak35 |  -.0701418   .0101739    -6.89
> 0.000    -.0900823   -.0502013
> > >        sak6g |  -.0407319   .0061695    -6.60
> 000    -.0528238     -.02864
> > >     lagemplo |   .4443965   .0139782    31.79   0.000     .4169997
> .4717933
> > >          age |   .0100016   .0052861     1.89   0.058    -.0003589
> .0203621
> > >        agesq |  -.0002096   .0000642    -3.26
> 0.001    -.0003356   -.0000837
> > >         east |   .0910558   .0149718     6.08   0.000     .0617116
> .1204
> > >    schoolmax |   .0081604   .0027614     2.96   0.003     .0027482
> .0135726
> > >         yr91 |  -.0255522   .0127857    -2.00
> 0.046    -.0506118   -.0004927
> > >         yr92 |   -.011285   .0128852    -0.88   0.381    -.0365396
> .0139695
> > >         yr93 |  -.0259762     .01303    -1.99
> 0.046    -.0515146   -.0004379
> > >         yr94 |  -.0032213   .0132016    -0.24   0.807    -.0290961
> .0226534
> > >         yr95 |  -.0055009   .0134236    -0.41   0.682    -.0318108
> .0208089
> > >         yr96 |  -.0257715   .0136532    -1.89   0.059    -.0525313
> .0009883
> > >         yr97 |  -.0123659   .0139074    -0.89   0.374    -.0396239
> .0148922
> > >        _cons |   .2832431   .1087164     2.61   0.009     .0701629
> .4963232
> >
> > -------------+------------------------------------------------------------
> ----
> > >     /sigma_u |   .1662792   .0073449    22.64   0.000     .1518834
> .180675
> > >     /sigma_e |   .2968988   .0025839   114.90   0.000     .2918345
> .3019632
> >
> > -------------+------------------------------------------------------------
> ----
> > >          rho |    .238768   .0173716                      .2060788
> .2741066
> >
> > --------------------------------------------------------------------------
> ----
> > > Likelihood ratio test of sigma_u=0: chibar2(01)=  229.39 Prob>=chibar2 =
> 0.000
> > >
> > >
> >
> > --------------------------------------------------------------------------
> -
> > >
> > >
> > > Thank you very much in advance for any idea,
> > >
> > > Enrica
> > >
> > >
> > > *
> > > *   For searches and help try:
> > > *   http://www.stata.com/support/faqs/res/findit.html
> > > *   http://www.stata.com/support/statalist/faq
> > > *   http://www.ats.ucla.edu/stat/stata/
> >
> >
> > Prof. Mark E. Schaffer
> > Director
> > Centre for Economic Reform and Transformation
> > Department of Economics
> > School of Management & Languages
> > Heriot-Watt University, Edinburgh EH14 4AS  UK
> > 44-131-451-3494 direct
> > 44-131-451-3008 fax
> > 44-131-451-3485 CERT administrator
> > http://www.som.hw.ac.uk/cert
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/support/faqs/res/findit.html
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
>
>
> ______________________________________
> Universidad Alberto Hurtado
> http://www.uahurtado.cl
> *
> *   For searches and help try:
> *   http://www.stata.com/support/faqs/res/findit.html
> *   http://www.stata.com/support/statalist/faq
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>
*
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