Hostname: page-component-74656f9884-54qdv Total loading time: 0 Render date: 2026-11-09T14:56:33.531Z Has data issue: false hasContentIssue false
  • English
  • Français

PANEL DATA MODELS WITH FINITE NUMBER OFMULTIPLE EQUILIBRIA

Published online by Cambridge University Press:  07 October 2009

Jinyong Hahn  and
Hyungsik Roger Moon
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We study a nonlinear panel data model in which thefixed effects are assumed to have finite support.The fixed effects estimator is known to have theincidental parameters problem. We contribute to theliterature by making a qualitative observation thatthe incidental parameters problem in this model maynot be not as severe as in the conventional case.Because fixed effects have finite support, theprobability of correctly identifying the fixedeffect converges to one even when the crosssectional dimension grows as fast as someexponential function of the time dimension. As aconsequence, the finite sample bias of the fixedeffects estimator is expected to be small.

Information

Type
Research Article
Information
Econometric Theory , Volume 26 , Issue 3 , June 2010 , pp. 863 - 881
Copyright
Copyright © Cambridge University Press 2009

Footnotes

We are thankful to Guido Kuersteiner and tworeferees for helpful comments. We also thankTimothy Derdenger for proofreading.

References

REFERENCES

Ackerberg, D.A. & Gowrisankaran, G. (2006) Quantifying equilibrium network externalities in the ACH banking industry. Rand Journal of Economics 37, 738–761.10.1111/j.1756-2171.2006.tb00040.xCrossRefGoogle Scholar
Aguirregabiria, V. & Mira, P. (2004) Sequential Estimation of Dynamic Discrete Games. Working paper, University of Toronto.Google Scholar
Arellano, M. & Hahn, J. (2007) Understanding bias in nonlinear panel models: Some recent developments. In Blundell, R., Newey, W.K., and Persson, T. (eds.), Advances in Economics and Econometrics. Cambridge University Press.Google Scholar
Bajari, P., Hong, H., & Ryan, S. (2004) Identification and Estimation of Discrete Games of Complete Information. NBER Working paper no. T0301.CrossRefGoogle Scholar
Bosq, D. (1993) Bernstein-type large deviations inequality for partial sums of strong mixing processes. Statistics 24, 59–70.10.1080/02331888308802389CrossRefGoogle Scholar
Bryc, W. (1992) On large deviations for uniformly strong mixing sequences. Stochastic Processes and their Applications 41, 191–202.10.1016/0304-4149(92)90120-FCrossRefGoogle Scholar
Carro, J. (2007) Estimating dynamic panel data discrete choice models with fixed effects. Journal of Econometrics 140, 503–528.10.1016/j.jeconom.2006.07.023CrossRefGoogle Scholar
Ciliberto, F. & Tamer, E. (2004) Market Structure and Multiple Equilibria in Airline Markets. Manuscript, University of Virginia and Northwestern University.Google Scholar
Dembo, A. & Zeitouni, O. (1998) Large Deviations Techniques and Applications. Springer-Verlag.10.1007/978-1-4612-5320-4CrossRefGoogle Scholar
Deuschel, J.D. & Stroock, D.W. (1989) Large Deviations. Academic Press.Google Scholar
Doukhan, P. (1995) Mixing: Properties and Examples. Lecture Notes in Statistics 85, Springer-Verlag.Google Scholar
Fernandez-Val, I. (2005) Bias Correction in Panel Data Models with Individual Specific Parameters. Working paper, Boston University.CrossRefGoogle Scholar
Hahn, J. & Kuersteiner, G. (2002) Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica 70, 1639–1657.10.1111/1468-0262.00344CrossRefGoogle Scholar
Hahn, J. & Kuersteiner, G. (2004) Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects. Working paper, UCLA.Google Scholar
Hahn, J. & Newey, W.K. (2004) Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72, 1295–1319.10.1111/j.1468-0262.2004.00533.xCrossRefGoogle Scholar
Leeb, H. & Pötscher, B.M. (2005) Model selection and inference: Facts and fiction. Econometric Theory 21, 21–59.10.1017/S0266466605050036CrossRefGoogle Scholar
Meyn, S.P. & Tweedie, R.L. (1993) Markov Chains and Stochastic Stability. Springer.10.1007/978-1-4471-3267-7CrossRefGoogle Scholar
Neyman, J. & Scott, E. (1948) Consistent estimates based on partially consistent observations. Econometrica 16, 1–31.10.2307/1914288CrossRefGoogle Scholar
Nickell, S. (1981) Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426.10.2307/1911408CrossRefGoogle Scholar
Pakes, A., Ostrovsky, M., & Berry, S. (2005) Simple Estimators for the Parameters of Discrete Dynamic Games. Harvard Institute of Economic Research Discussion Paper no. 2036.Google Scholar
Pollard, D. (2002) A User’s Guide to Measure Theoretic Probability. Cambridge University Press.Google Scholar
Sweeting, A. (2004) Coordination Games, Multiple Equilibria and the Timing of Radio Commercials. Working paper, Northwestern University.Google Scholar
Woutersen, T. (2002) Robustness Against Incidental Parameters. Working paper, University of Western Ontario.Google Scholar