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st: RE: "Crude" Random Effects Estimates

From   "Dean DeRosa" <>
To   <>
Subject   st: RE: "Crude" Random Effects Estimates
Date   Thu, 1 Jun 2006 08:14:11 -0400

Rodrigo and Steve,

Thank you for your replies to my posts regarding tenable approaches to
deriving "constrained" RE estimates. I find the Thomas Plumper-Vera Troeger
(2004) paper on estimating time-invariant variables in FE models
particularly appealing because it follows a similar vein as I had in mind,
but adds statistical rigor and an available Stata .ado routine to boot. I
highly commend the paper and routine to others working with FE vs. RE models
and panel data sets, for consideration and further evaluation.


Dean DeRosa


Date: Tue, 30 May 2006 10:11:13 -0400
From: "Rodrigo A. Alfaro" <>
Subject: st: Re: RE: "Crude" Random Effects Estimates


I understood that you approach does not use IV variables. So far, I don't 
know an algorithm to deal with random-effects and time-invariant variables. 
A different approach (to IV) was developed by Pluemper-Vega (2004) "The 
Estimation of Time-Invariant Variables in Panel Analyses with Unit Fixed 

I hope this helps you


Date: Wed, 31 May 2006 03:00:57 +1200
From: "Steve Stillman" <>
Subject: st: "Crude" Random Effects Estimates

You may want to read about correlated random effects models.  This is a more
econometrically sounds approach that accomplishes what you are trying to do.
Below are a number of citations that reference these types of models.  Some
of these can be estimated in stata using SUREG and constraints.  Others
require a minimum distance approach or the application of non-linear
constraints, neither of which is straightforward to do in stata to my


Mundlak, Yair (1978), "On the Pooling of Time Series and Cross-section
Data", Econometrica, 46, 69-85.

Chamberlain, Gary (1984), "Panel Data", Handbook of Econometrics, Chapter 22
in Vol. 2, 1247-1318, Elsevier Science B.V.

Ashenfelter, Orley and David J. Zimmerman (1997), "Estimates of the Return
to Schooling From Sibling Data: Fathers, Sons and Brothers", The Review of
Economics and Statistics, Vol. 79(1), February, .

Vella, Frank and M. Verbeek (1998), "Whose Wages Do Unions Raise?  A Dynamic
Model of Unionism and Wage Rate Determination for Young Men", Journal of
Applied Econometrics, 13, 163-183.

-----Original Message-----
From: Dean DeRosa [] 
Sent: Monday, May 29, 2006 12:16 PM
To: ''
Subject: RE: "Crude" Random Effects Estimates


Thank you for your reply to my post. Perhaps my subject line should have
been more appropriately titled "Constrained" random effect estimates.

I am looking for a reasonably practical and straightforward way of
correcting to some degree for the possible covariance between unobserved and
observed explanatory variables in the random effects variant of my large
gravity trade model, without having to apply a Hausman-Taylor or other
instrumental variables approach. Thus, I am experimenting with constraining
the random effects estimates to be equal to the fixed effects estimates for
time-variant variables (through corresponding offsets to the dependent
variable), leaving the time-invariant explanatory variables to be the sole
remaining source of possible covariance between unobserved and observed
explanatory variables in the model. Unfortunately, this approach does not
allow further appeal to the Hausman specification test. However, I find on
applying the approach to the empirical example in Table 7.4, p.129, of
Baltagi's 3rd edition textbook (Econometric analysis of panel data) that the
resulting coefficient estimates for the time-invariant variables are very
close to those reported by Baltagi using the Hausman-Taylor approach. Hardly
a formal monte carlo test of my approach, but interesting results

Dean DeRosa

Date: Thu, 25 May 2006 11:20:23 -0400
From: "Rodrigo A. Alfaro" <>
Subject: st: Re: "Crude" Random Effects Estimates

Dear Dean

HT is computed in 3 steps: (1) FE for time-variant, (2) IV for 
time-invariant and (3) IV for both (where the variables have the GLS 
transformation to control for the random effect). As it is discussed in the 
paper (Econometrica, vol 49 n6 1981, 1377-1398) the last step is to compute 
efficient estimators. In (1) you have consistent estimators for time-variant

variables, with these you compute a proxy of the unobservable and run a 
regression of this proxy against time-invariant variables using instruments 
(2). These estimators (for time-invariant variables) are also consistent. A 
technical paper of Hahn and Meinecke (Econometric Theory 21, 2005. 455-469) 
shows that we still have consistency for non-linear models (a generalization

of HT). In conclusion, you can force the FE coefficient for the time variant

variables... but you will need to compute a IV regression for the 
time-invariant (in the second step as you suggest) dealing with the decision

of instruments. Note that in the case of (manually) two-step regression you 
can include other instruments that are not in the model.

For practical purposes, I suggest you to run a FE model and compare the 
coefficients of the time-variant variables with HT. If they are different 
you can gain something doing the 2-step procedure. In addition, find other 
exogenous variables (time-invariant) that can be used in the second step. 
Once, you estimate both set of parameters you have to compute the standard 
error for 2-steps. Maybe you could be interested in robust-estimation of 
that. Wooldridge textbook offers the formulas to compute it.


-----Original Message-----
From: Dean DeRosa [] 
Sent: Thursday, May 25, 2006 10:45 AM
To: ''
Subject: "Crude" Random Effects Estimates

I am estimating the parameters of a gravity trade model, using a large panel
data set of international trade flows and explanatory variables. A number of
the explanatory variables are time-invariant, so I am mainly interested in
obtaining random effects (within cum between) estimates. I am experimenting
with Hausman-Taylor (HT) estimates using -xthtaylor- but so far find these
estimates difficult to evaluate given that different combinations of
endogenous (versus instrumental) variables lead to a variety of coefficient
estimates for the time-varying explanatory variables, with no decisive, or
best, outcome in terms of the Hausman test of the difference between the HT
and within estimates.

My query is whether it is tenable to run the random effects regression
command -xtreg, re- constraining the coefficient estimates for the
time-varying explanatory variables to be equal to "first-stage" fixed
effects (within) estimates. Per force, this would seem to eliminate possible
correlation between the time-varying expanatory variables and the
unobservable specific effect variable, and to obviate the necessity of
evaluating the random effects estimates using the -hausman- test. But, would
it still leave the "second stage" random effects estimates subject to
possible correlation between the time-invariant explanatory variables and
the unobservable specific effect variable? Also, is there any precedent in
the panel data literature for pursuing such a crude approach to obtaining
random effects estimates?

Dean DeRosa

Dean A. DeRosa
200 Park Avenue, Suite 306
Falls Church, Virginia 22046 USA
Tel: 703 532-8510 | Skype V-Tel: ADRintl | |

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