RE: st: Re: RE: RE: RE: time random effects

["ALICE DOBSON" <alice_dobson@hotmail.com>]

Hi Giovanni & other statalisters,
this might not be exactly a stata issue and has already been discussed many 
times. But, it gets more confusing each time. For instance, below you 
explain that xtreg, re estimates a random effect model with cross-section 
heterogeneity?

Further, from what little understanding I have, HLM (xtmixed or gllamm) 
enables us to model the distribution (or variation) of both the intercept 
and slope in a panel data set. More clearly, it enables us to understand the 
across subjects and within subject change in the outcome variable over a 
particular time.

For the last couple of days, there have been queries about two-way random 
effects and two-way fixed effects, which has left me even more confused.

Is there any good source where I can relearn these issues?

Best,
Alice



> From: Giovanni Bruno <giovanni.bruno@uni-bocconi.it>
> Reply-To: statalist@hsphsun2.harvard.edu
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Re: RE: RE: RE: time random effects
> Date: Thu, 23 Jun 2005 16:24:31 +0200
>
> As Scott Merryman clearly suggested, -xtreg- can only estimate one-way
> *random* effect models, with either time or cross-section heterogeneity.
>  -xtmixed-, however, can easily estimate the two-way random effect
> panel data model.
>
> As explained in [Baltagi (2005), Econometric analysis of panel data, ch. 3]
> there are various ways to estimate the two-way random effect model in
> econometrics. Using the Grunfeld's data set
>
> <http://www.wiley.com/legacy/wileychi/baltagi3e/data_sets.html>
>
> the following -xtmixed- instruction produces  estimates for parameters and
> standard deviations that are identical to those reported in Baltagi's 
> (2005)
> Table 3.1 under the IMLE (iterated maximum likelihood estimator) method,
> implemented by Baltagi using TSP (FN=firm index; YR=time index; 
> I=investments;
> F=value of the firm; K=capital stock):
>
>
> . xtmixed I F K || _all: R.FN || _all: R.YR,mle
>
> Performing EM optimization:
>
> Performing gradient-based optimization:
>
> Iteration 0:   log likelihood = -1095.3809
> Iteration 1:   log likelihood = -1095.2502
> Iteration 2:   log likelihood = -1095.2485
> Iteration 3:   log likelihood = -1095.2485
>
> Computing standard errors:
>
> Mixed-effects ML regression                     Number of obs      =       
> 200
> Group variable: _all                            Number of groups   =        
>  1
>
>                                               Obs per group: min =       
> 200
>                                                              avg =     
> 200.0
>                                                              max =       
> 200
>
>
>                                               Wald chi2(2)       =    
> 661.07
> Log likelihood = -1095.2485                     Prob > chi2        =    
> 0.0000
>
> ------------------------------------------------------------------------------
>          I |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
> Interval]
> -------------+----------------------------------------------------------------
>          F |   .1099009   .0103779    10.59   0.000     .0895606    
> .1302413
>          K |   .3092262   .0172179    17.96   0.000     .2754798    
> .3429726
>      _cons |  -58.27126   27.76275    -2.10   0.036    -112.6853   
> -3.857264
> ------------------------------------------------------------------------------
>
> ------------------------------------------------------------------------------
> Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. 
> Interval]
> -----------------------------+------------------------------------------------
> _all: Identity               |
>                   sd(R.FN) |   80.41164   18.42471       51.3196    
> 125.9954
> -----------------------------+------------------------------------------------
> _all: Identity               |
>                   sd(R.YR) |   3.860627   15.29474      .0016384    
> 9096.692
> -----------------------------+------------------------------------------------
>               sd(Residual) |   52.34756   2.904361       46.9537    
> 58.36104
> ------------------------------------------------------------------------------
> LR test vs. linear regression:       chi2(2) =   193.11   Prob > chi2 = 
> 0.0000
>
> Note: LR test is conservative and provided only for reference
>
>
> Giovanni
>
>
>
> Scrive Wanli Zhao <zhaowl@temple.edu>:
>
> > Thank you, Scott. Without disrespect, I am still a little bit unsure 
> about
> > this. Several small points raise my concern. On the -help xtreg- page in
> > Stata, on the bottom are some command examples and none of them show 
> time
> > random effects explicitly. Also, on the -findit xtreg- page, there is an
> > example for chapter 14 of Greene's book. I checked it, the original text
> > book chapter has two way effects in the table. On the Stata webpage for
> > this, seems that it stops on the time random effects part. In addition,
> > seems that when you do not specify i() in xtreg (but you specify panel 
> tis
> > beforehand) and estimate random effects, it means only the cross-section
> > random effects, not both. I hope you could enlighten me. Just say you 
> tried
> > this before. :-)
> >
> > Wanli
> >
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Scott 
> Merryman
> > Sent: Wednesday, June 22, 2005 8:38 PM
> > To: statalist@hsphsun2.harvard.edu
> > Subject: st: RE: RE: time random effects
> >
> > If you want random time effects without cross section effects you can 
> use
> > -xtreg-.  Simply specify the "i(varname)" option with the time variable
> > (i.e.  -xtreg depvar indepvar, i(time)-)
> >
> > For two-way random effects take a look at -xtmixed- or -gllamm-
> >
> > Scott
> >
> >
> > > -----Original Message-----
> > > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> > > statalist@hsphsun2.harvard.edu] On Behalf Of Wanli Zhao
> > > Sent: Wednesday, June 22, 2005 3:52 PM
> > > To: statalist@hsphsun2.harvard.edu
> > > Subject: st: RE: time random effects
> > >
> > > I asked the same question before and stared at the list every day and
> > > got no reply. I did some homework and found people say that Stata can
> > > do the two-way effects panel model (error component model by another
> > > name). I still cannot figure out how to do it in Stata. Adding time
> > > dummies to do fixed effects is simple (with/without cross-section
> > > effects). But how to do time random effects, with/without
> > > cross-section effects? In the literature and text books, error
> > > component model with time variation and cross-section variation is
> > > just there. If you know how to do it in Stat, pls help.
> > > Thanks
> > > a lot.
> > >
> > > Wanli Zhao
> > > Using Stata 9
> > >
> > >
> > > *
> > > *   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/
> >
> >
> > *
> > *   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/
> >
> >
> > *
> > *   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/
> >
>
>
> --
> Giovanni Bruno
> Istituto di Economia Politica, Universit� Bocconi
> Via U. Gobbi, 5, 20136 Milano
> Italy
> tel. + 02 5836 5411
> fax. + 02 5836 5438
> *
> *   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/
_________________________________________________________________
Don�t just search. Find. Check out the new MSN Search! 
http://search.msn.click-url.com/go/onm00200636ave/direct/01/

*
*   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/