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Re: st: using xtabond and xtabond2

From   Michael Hanson <>
Subject   Re: st: using xtabond and xtabond2
Date   Sat, 28 Jul 2007 19:55:08 -0400

On Jul 28, 2007, at 6:56 PM, natalie chan wrote:

Maybe this is  a question more about econometrics than about Stata but
I can't find anywhere more appropriate to ask this question. Thanks in

I am doing regressions on economic growth equations with a panel data
of 20 years for 48 countries. I wanted to use dynamic panel approach
with xtabond or xtabond2, however, the Arellano-Bond methods are
specified for data with small T and large N.  On the other hand, I
have seen some researchers using Arellano-Bond methods on growth
models, including Bond himself. Could anyone give me some advice on
this? Thanks a lot.
I would like to expand Natalie's question: I have an application of dynamic panel data in which T/N is nearly 3, with N = 50. In David Roodman's excellent discussion of -xtabond2- [1], he writes, "If T is large, dynamic panel bias becomes insignificant, and a more straightforward fixed effects estimator works." (p. 42) However, I have never been able to find a discussion of how "large" of a T is "large enough" in the literature (which I interpret is part of Natalie's question). In the only textbook reference I have found, Baltagi (2005) [2] writes, "FE, GMM, and LIML exhibit a bias term in their asymptotic distributions; the biases are of the order 1/T, 1/N, and 1/(2N-T), respectively." (p. 153)

Would it be reasonable, therefore, to conclude that in Natalie's case (T/N < 1/2), GMM (i.e. AB-type estimators) or LIML would be preferred, whereas in my case (T/N > 2.5), FE would be preferred? (I realize that this claim is based on asymptotic arguments, and that the N & T discussed here are probably too small. Any information about the small-sample properties of these estimators in a dynamic panel context would be appreciated as well.)

I also recognize that this question is at least as much about statistics (econometrics) as about Stata, and I appreciate any help or suggestions.

[1] <>
[2] <>

-- Mike

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