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


From   "Wanli Zhao" <zhaowl@temple.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Re: RE: RE: RE: time random effects
Date   Thu, 23 Jun 2005 11:48:59 -0400

Giovanni,
Thank you so much. Xtmixed is the command.

Wanli 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Giovanni Bruno
Sent: Thursday, June 23, 2005 10:25 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Re: RE: RE: RE: time random effects

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, UniversitÓ 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/



*
*   For searches and help try:
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*   http://www.ats.ucla.edu/stat/stata/



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