Dean, Rodrigo,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Dean DeRosa
> Sent: 01 June 2006 13:14
> To: [email protected]
> Subject: st: RE: "Crude" Random Effects Estimates
>
> 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.
I saw Rodrigo's post, was intrigued, and read the paper, and to be
honest I had trouble understanding what they were proposing. I couldn't
work out what orthogonality conditions their estimator used that aren't
used by the within, between or random effects estimator. Can you help
here?
Also, I thought it would be helpful to compare their proposed estimator
to the between estimator; they look closely related. The former is the
standard estimator for getting at effects caused by characteristics that
are constant within groups.
Just my 0.02!
Cheers,
Mark
> http://scholar.google.com/url?sa=U&q=http://www.soz.unibe.ch/s
tudium/ws0506/
> downloads/Plumper%2520paper-fixed%2520effects%2520PAFE_59_l.pdf
>
> Regards,
>
> Dean DeRosa
>
>
>
> -----------------------------------
>
> Date: Tue, 30 May 2006 10:11:13 -0400
> From: "Rodrigo A. Alfaro" <[email protected]>
> Subject: st: Re: RE: "Crude" Random Effects Estimates
>
> Dean,
>
> 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 Effects".
>
> I hope this helps you
> Rodrigo.
>
> ----------------------------------
>
> Date: Wed, 31 May 2006 03:00:57 +1200
> From: "Steve Stillman" <[email protected]>
> Subject: st: "Crude" Random Effects Estimates
>
> Dean,
> 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 knowledge.
>
> Cheers,
> Steve
>
> 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 [mailto:[email protected]]
> Sent: Monday, May 29, 2006 12:16 PM
> To: '[email protected]'
> Subject: RE: "Crude" Random Effects Estimates
>
>
>
> Rodrigo,
>
> 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
> nonetheless.
>
> Dean DeRosa
>
>
>
> Date: Thu, 25 May 2006 11:20:23 -0400
> From: "Rodrigo A. Alfaro" <[email protected]>
> 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.
>
> Rodrigo.
>
>
>
> -----Original Message-----
> From: Dean DeRosa [mailto:[email protected]]
> Sent: Thursday, May 25, 2006 10:45 AM
> To: '[email protected]'
> 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
>
>
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