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From |
"ALICE DOBSON" <alice_dobson@hotmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: Re: RE: RE: RE: time random effects |

Date |
Thu, 30 Jun 2005 09:09:51 -0400 |

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, Università 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:

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* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

**References**:**st: Re: RE: RE: RE: time random effects***From:*Giovanni Bruno <giovanni.bruno@uni-bocconi.it>

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