International Statistical Review

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

Mark N. Harris and László Mátyás

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek-Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.

Article information

Source
Internat. Statist. Rev., Volume 72, Number 3 (2004), 397-408.

Dates
First available in Project Euclid: 8 December 2004

Permanent link to this document
https://projecteuclid.org/euclid.isr/1102516479

Keywords
Panel data Dynamic models Monte Carlo IV and GMM estimators

Citation

Harris, Mark N.; Mátyás, László. A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models. Internat. Statist. Rev. 72 (2004), no. 3, 397--408. https://projecteuclid.org/euclid.isr/1102516479


Export citation

References

  • [1] Ahn, S. & Schmidt, P. (1999). Estimation of Linear Panel Data Models Using GMM. In Generalised Method of Moments Estimation, Ed. L. Mátyás, pp. 211-245. Cambridge, U.K.: Cambridge University Press. Abstract can also be found in the ISI/STMA publication
  • [2] Ahn, S. & Schmidt, P. (1995). Efficient Estimation of Models for Dynamic Panel Data. J. Econometrics, 68(1), 5-27. Abstract can also be found in the ISI/STMA publication
  • [3] Ahn, S. & Schmidt, P. (1997). Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation. J. Econometrics, 76(1-2), 309-321. Abstract can also be found in the ISI/STMA publication
  • [4] Altonji, J. & Segal, L. (1996). Small-Sample Bias in {GMM} Estimation of Covariance Structures. J. Business and Economic Statistics, 14(3), 353-366.
  • [5] Amemiya, T. & MaCurdy, T. (1986). Instrumental Estimation of an Error Components Model. Econometrica, 54, 869-881.
  • [6] Anderson, T. & Hsiao, C. (1982). Formulation and Estimation of Dynamic Models Using Panel Data. J. Econometrics, 18, 578-606.
  • [7] Arellano, M. (1988). A Note on the Anderson-Hsiao Estimator for Panel Data. Discussion paper, mimeo, Institute of Economics, Oxford University.
  • [8] Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte-Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58, 127-134.
  • [9] Arellano, M. & Bover, O. (1995). Another Look at the Instrumental Variables Estimation of Error-Components Models.\linebreak J. Econometrics, 68(1), 29-51.
  • [10] Arellano, M. & Honoré, B. (2001). Panel Data Models: Some Recent Developments. In Handbook of Econometrics, Eds. \linebreak J. Heckman and E. Leamer, vol. 5, ch. 53. Amsterdam: North Holland/Elsevier Science B.V.
  • [11] Balestra, P. & Nerlove, M. (1966). Pooling Cross-Section and Time-Series Data in the Estimation of a Dynamic Model. Econometrica, 34, 585-612.
  • [12] Blundell, R. & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. J. Econometrics, 87, 115-143. Abstract can also be found in the ISI/STMA publication
  • [13] Breitung, J. & Lechner, M. (1999). Alternative {GMM} Methods for Nonlinear Panel Data Models. In Generalised Method of Moments Estimation, Ed. L. Mátyás, pp. 248-274. Cambridge, U.K.: Cambridge University Press.
  • [14] Breusch, T., Mizon, G. & Schmidt, P. (1989). Efficient Estimation Using Panel Data. Econometrica, 57, 695-700. Abstract can also be found in the ISI/STMA publication
  • [15] Chamberlain, G. (1982). Multivariate Regression Models for Panel Data. J. Econometrics, 18, 5-46.
  • [16] Crepon, B., Kramarz, F. & Trognon, A. (1998). Parameters of Interest, Nuisance Parameter and Orthogonality Conditions: An Application to Autoregressive Error Component Models. J. Econometrics, 82(1), 135-156. Abstract can also be found in the ISI/STMA publication
  • [17] Hansen, L. (1982). Large Sample Properties of Generalised Method of Moments Estimators. Econometrica, 50, 1029-1054.
  • [18] Harris, M. & Mátyás, L. (2000). Performance of the Operational Wansbeek-Bekker Estimator for Dynamic Panel Data Models. Applied Economics Letters, 7(3), 149-153.
  • [19] Hausman, J. & Taylor, W. (1981). Panel Data and Unobservable Individual Effects. Econometrica, 49, 1377-1398.
  • [20] Kinal, T. (1980). The Existence of K-Class Estimators. Econometrica, 48, 241-249.
  • [21] Kiviet, J. (1995). On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models. J. Econometrics, 68(1), 53-78. Abstract can also be found in the ISI/STMA publication
  • [22] Nickell, S. (1981). Biases in Models With Fixed Effects. Econometrica, 49, 1417-1426.
  • [23] Sevestre, P. & Trognon, A. (1985). A Note on Autoregressive Error Component Models. J. Econometrics, 28, 231-245.
  • [24] Sevestre, P. & Trognon, A. (1996). Dynamic Linear Models. In The Econometrics of Panel Data, Eds. L. Mátyás and \linebreak P. Sevestre, ch. 7, pp. 120-144. The Netherlands: Kluwer Academic Publishers.
  • [25] Wansbeek, T. & Bekker, P. (1996). On IV, GMM and ML in a Dynamic Panel Data Model. Economic Letters, 51(2), 145-152.
  • [26] Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Massachusetts, U.S.A.: The MIT Press.