Kit and Yuluen,
I would not recommend using Arellano-Bond (AB) routinely too. For a small T (number of time periods), AB is fine, whatever is the theoretical model behind.
For moderate or large T, I suggest to truncate the number of lags used in AB (call this estimator TAB) or you can use LDV+FE instead. The bias of the latter decreases on T (see note 1). Also, TAB is almost unbiased, but it is less efficient than AB (see note 2).
In conclusion, I suggest to start with a estimator that agrees your theoretical model as first choice. If there is "no-model" but endogeneity then you can use IV estimators as Kit suggested.
Best regards,
Rodrigo.
Notes:
(1) The early paper on the bias of LDV+FE is Nickell, S. (1981) "Biases in Dynamic Models with Fixed Effeects," Econometrica, 49, 1417-1426. For moderate or large T, you have Hahn, J., and Kuersteiner, G. (2002) "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T are Large," Econometrica, 70,
1639-1657 and Alvarez, J., and Arellano, M. (2003) "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data," Econometrica, 71, 1121-1159. A more friendly paper is Beck, N., and Katz, J. (2004) "Time-Series-Cross-Section Issues: Dynamics," Working Paper, New York University, probably published in some Political Science journal. A good survey on Panel Danel that deals this material is Arellano, M. (2003) Panel Data Econometrics, New York: Oxford University Press.
(2) A practical issue on AB when T is moderate or large is to truncate the number of lags used as instruments. I found that this practical procedure reduces the finite-sample bias of AB (see pages 7/8 and the appendix A.2 of my WP available at http://www.bcentral.cl/eng/studies/working-papers/pdf/dtbc467.pdf). However, Monte Carlo simulations (not reported) show that the TAB is less efficient than AB, then there is a trade-off between finite-sample bias and efficiency. That could be solved by finding the optimum lag that miminizes the MSE. I am working (in my sparse time = low priority) on that, but it will require a balanced panel.
-----Mensaje original-----
De: Kit Baum [mailto:[email protected]]
Enviado el: Mi�rcoles, 14 de Mayo de 2008 07:11 p.m.
Para: Rodrigo Alfaro A.
CC: [email protected]
Asunto: Re: GMM or 2SLS with cluster adjustment or others
Rodrigo,
The point I was trying to make is that the fixed effects estimator (e.g. xtivreg, fe) is biased in the presence of a LDV, and so Arellano-Bond should be used in that context. There is nothing to prevent you from using Arellano-Bond in an equation without a LDV, but given the complexity of the A-B methodology (and possibility to generate hundreds of instruments) I would not recommend using it routinely on a static panel data model. In that context it is certainly appropriate to use IV-GMM, as is provided by xtivreg2, fe with the gmm2s option. That is also a GMM estimator which will be more efficient than standard IV.
Best wishes
Kit
Kit Baum, Boston College Economics and DIW Berlin http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
On May 14, 2008, at 18:39 , Rodrigo Alfaro A. wrote:
>
> Kit wrote:
> The Arellano-Bond / Arellano-Bover / Blundell-Bond methodology
> (xtabond,
> xtabond2) is appropriate if you have lagged dependent variables, and
> unnecessary otherwise.
>
> I am not agree with this statement. Equation (13) in the classic paper
> Blundell, Bond, Devereux, and Schiantarelli (1992) "Investment and
> Tobin's Q: Evidence from company panel data" Journal of Econometrics
> 51
> (1992) 233-257 does not have a lagged dependent variable, but it is
> also estimated by GMM.
>
> Rodrigo.
>
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