Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-25T08:02:19.343Z Has data issue: false hasContentIssue false
  • English
  • Français

REDUCING BIAS OF MLE IN A DYNAMIC PANEL MODEL

Published online by Cambridge University Press:  15 March 2006

Jinyong Hahn  and
Hyungsik Roger Moon
Jinyong Hahn
Affiliation:
UCLA
Hyungsik Roger Moon
Affiliation:
University of Southern California
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using “large n and large T” asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive its asymptotic bias. We find that the same higher order bias correction approach proposed by Hahn and Kuersteiner (2002, Econometrica 70, 1639–1659) can be applied to the dynamic linear panel model even when time specific effects are present.We thank Peter Phillips and three anonymous referees for helpful comments. The first author gratefully acknowledges financial support from NSF grant SES-0313651. The second author appreciates the Faculty Development Awards of USC for research support.

Type
MISCELLANEA
Information
Econometric Theory , Volume 22 , Issue 3 , June 2006 , pp. 499 - 512
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahn, S.C., Y.H. Lee, & P. Schmidt (2001) GMM estimation of linear panel data models with time-varying individual effects. Journal of Econometrics 101, 219–255.Google Scholar
Alvarez, J. & M. Arellano (2003) The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71, 1121–1160.Google Scholar
Arellano, M. & B. Honoré (2001) Panel data models: Some recent developments. In J. Heckman & E. Leamer (eds.), Handbook of Econometrics, vol. 5, pp. 3229–3296. Elsevier Science.
Bai, J. & S. Ng (2004) A PANIC attack on unit roots and cointegration. Econometrica 72, 1127–1177.Google Scholar
Brillinger, D. (1981) Time Series: Data Analysis and Theory. Holden-Day.
Hahn, J. & G. Kuersteiner (2002) Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica 70, 1639–1659.Google Scholar
Hahn, J. & G. Kuersteiner (2003) Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects. Mimeo, UCLA.
Hahn, J. & W. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72, 1295–1319.Google Scholar
Holtz-Eakin, D., W. Newey, & H. Rosen (1988) Estimating vector autoregressions with panel data. Econometrica 56, 1371–1395.Google Scholar
Hsiao, C. (2003) Analysis of Panel Data, 2nd ed. Cambridge University Press.
Kiviet, J. (1995) On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics 68, 53–78.Google Scholar
Moon, H.R. & B. Perron (2004) Testing for a unit root in panels with dynamic factors. Journal of Econometrics 122, 81–126.Google Scholar
Moon, H.R. & P.C.B. Phillips (2004) GMM estimation of autoregressive roots near unity with panel data. Econometrica 72, 467–522.Google Scholar
Neyman, J. & E. Scott (1948) Consistent estimates based on partially consistent observations. Econometrica 16, 1–31.Google Scholar
Nickell, S.J. (1981) Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426.Google Scholar
Phillips, P.C.B. & H.R. Moon (1999) Linear regression limit theory for nonstationary panel data. Econometrica 67, 1057–1111.Google Scholar
Phillips, P.C.B. & D. Sul (2003a) Dynamic panel estimation and homogeneity testing under cross section dependence. Econometric Journal 6, 217–259.Google Scholar
Phillips, P.C.B. & D. Sul (2003b) Bias in Dynamic Panel Estimation with Fixed Effects, Incidental Trends and Cross Section Dependence. Mimeo, Yale University and University of Auckland.