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A SPATIAL DYNAMIC PANEL DATA MODEL WITHBOTH TIME AND INDIVIDUAL FIXEDEFFECTS

Published online by Cambridge University Press:  18 August 2009

Lung-fei Lee  and
Jihai Yu
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Abstract

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This paper establishes asymptotic properties ofquasi-maximum likelihood estimators for spatialdynamic panel data with both time and individualfixed effects when the number of individualsn and the number of time periodsT can be large. We propose a datatransformation approach to eliminate the timeeffects. When n / T → 0, theestimators are $\root \of {nT}$ consistent andasymptotically centered normal; whenn is asymptotically proportionalto T, they are $\root \of {nT}$ consistent andasymptotically normal, but the limit distribution isnot centered around 0; when n / T →∞, the estimators are consistent with rateT and have a degenerate limitdistribution. We also propose a bias correction forour estimators. When n1/3 /T → 0, the correction willasymptotically eliminate the bias and yield acentered confidence interval. The estimates from thetransformation approach can be consistent whenn is a fixed finite number.

Information

Type
Research Article
Information
Econometric Theory , Volume 26 , Issue 2 , April 2010 , pp. 564 - 597
Copyright
Copyright © Cambridge University Press 2009

Footnotes

We thank two anonymous referees and the co-editorJinyong Hahn for their comments and suggestionsfor improving this paper. Lee acknowledgesfinancial support from NSF under grantSES-0519204.

References

REFERENCES

Alvarez, J. & Arellano, M. (2003) The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71, 1121–1159.10.1111/1468-0262.00441CrossRefGoogle Scholar
Amemiya, T. (1971) The estimation of the variances in a variance-components model. International Economic Review 12, 1–13.CrossRefGoogle Scholar
Anselin, L. (1988) Spatial Econometrics: Methods and Models. Kluwer Academic, The Netherlands.CrossRefGoogle Scholar
Baltagi, B.H., Song, S.H., & Koh, W. (2003) Testing panel data regression models with spatial error correlation. Journal of Econometrics 117, 123–150.10.1016/S0304-4076(03)00120-9CrossRefGoogle Scholar
Case, A.C. (1991) Spatial patterns in household demand. Econometrica 59, 953–965.CrossRefGoogle Scholar
Conley, T.G. & Dupor, W. (2003) A spatial analysis of sectoral complementarity. Journal of Political Economy 111, 311–352.10.1086/367681CrossRefGoogle Scholar
Ertur, C. & Koch, W. (2007) Growth, technological interdependence and spatial externalities: Theory and evidence. Journal of Applied Econometrics 22, 1033–1062.CrossRefGoogle Scholar
Foote, C.L. (2007) Space and Time in Macroeconomic Panel Data: Young Workers and State-Level Unemployment Revisited. Working paper 07–10, Federal Reserve Bank of Boston.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.CrossRefGoogle Scholar
Hahn, J. & Moon, H.R. (2006) Reducing bias of MLE in a dynamic panel model. Econometric Theory 22, 499–512.10.1017/S0266466606060245CrossRefGoogle Scholar
Hahn, J. & Newey, W. (2004) Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72, 1295–1319.CrossRefGoogle Scholar
Hamilton, J.D. (1994) Time Series Analysis. Princeton University Press.10.1515/9780691218632CrossRefGoogle Scholar
Kapoor, M., Kelejian, H.H., & Prucha, I.R. (2007) Panel data models with spatially correlated error components. Journal of Econometrics 140, 97–130.10.1016/j.jeconom.2006.09.004CrossRefGoogle Scholar
Kelejian, H.H. & Prucha, I.R. (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbance. Journal of Real Estate Finance and Economics 17, 99–121.CrossRefGoogle Scholar
Kelejian, H.H. & Prucha, I.R. (2001) On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics 104, 219–257.CrossRefGoogle Scholar
Kelejian, H.H. & Prucha, I.R. (2007) Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances. Working paper, University of Maryland.CrossRefGoogle Scholar
Lee, L.F. (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial econometric models. Econometrica 72, 1899–1925.10.1111/j.1468-0262.2004.00558.xCrossRefGoogle Scholar
Lee, L.F. (2007) GMM and 2SLS estimation of mixed regressive, spatial autoregressive models. Journal of Econometrics 137, 489–514.10.1016/j.jeconom.2005.10.004CrossRefGoogle Scholar
Lee, L.F. & Yu, J. (2007) Near Unit Root in the Spatial Autoregressive Model. Working paper, Ohio State University.Google Scholar
Lin, X. & Lee, L.F. (2005) GMM Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity. Working paper, Ohio State University.Google Scholar
Nerlove, M. (1971) A note on error components models. Econometrica 39, 383–396.CrossRefGoogle Scholar
Ord, J.K. (1975) Estimation methods for models of spatial interaction. Journal of the American Statistical Association 70, 120–126.10.1080/01621459.1975.10480272CrossRefGoogle Scholar
Phillips, P.C.B. & Moon, H. (1999) Linear regression limit theory for nonstationary panel data. Econometrica 67, 1057–1112.CrossRefGoogle Scholar
Rothenberg, T.J. (1971) Identification in parametric models. Econometrica 39, 577–591.CrossRefGoogle Scholar
Theil, H. (1971) Principles of Econometrics. Wiley.Google Scholar
Wallace, T.D. & Hussain, A. (1969) The use of error components models in combining cross-section and time-series data. Econometrica 37, 55–72.10.2307/1909205CrossRefGoogle Scholar
Yu, J., de Jong, R., & Lee, L.F. (2007) Quasi-Maximum Likelihood Estimators for Spatial Dynamic Panel Data with Fixed Effects when both n and T Are Large: A Nonstationary Case. Working paper, Ohio State University.CrossRefGoogle Scholar
Yu, J., de Jong, R., & Lee, L.F. (2008) Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. Journal of Econometrics 146, 118–134.10.1016/j.jeconom.2008.08.002CrossRefGoogle Scholar
Yu, J. & Lee, L.F. (2007) Estimation of Unit Root Spatial Dynamic Panel Data Models. Working paper, Ohio State University.Google Scholar