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THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS

Published online by Cambridge University Press:  25 March 2011

Sílvia Gonçalves
Sílvia Gonçalves*
Affiliation:
CIREQ, CIRANO, and Université de Montréal
*
*Address correspondence to Sílvia Gonçalves, Département de sciences économiques, CIREQ and CIRANO, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, QC, H3C 3J7, Canada; e-mail: [email protected].
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Abstract

In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.

Type
ARTICLES
Information
Econometric Theory , Volume 27 , Issue 5: SPECIAL ISSUE ON BOOTSTRAP AND NUMERICAL METHODS IN TIME SERIES , October 2011 , pp. 1048 - 1082
Copyright
Copyright © Cambridge University Press 2011

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