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A bootstrap procedure for panel data sets with many cross-sectional units

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  • G. Kapetanios
Abstract
This paper considers the issue of bootstrap resampling in panel data sets. The availability of data sets with large temporal and cross-sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross-sectional units with replacement. In cases where the data do not exhibit cross-sectional dependence but exhibit temporal dependence, such a resampling scheme is of great interest as it allows the application of i.i.d. bootstrap resampling rather than block bootstrap resampling. It is well known that the former enables superior approximation to distributions of statistics compared to the latter. We prove that the bootstrap based on cross-sectional resampling provides asymptotic refinements. A Monte Carlo study illustrates the superior properties of the new resampling scheme compared to the block bootstrap. Copyright © 2008 The Author. Journal compilation © Royal Economic Society 2008

Suggested Citation

  • G. Kapetanios, 2008. "A bootstrap procedure for panel data sets with many cross-sectional units," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 377-395, July.
    • Handle: RePEc:ect:emjrnl:v:11:y:2008:i:2:p:377-395
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    References listed on IDEAS

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    1. Donald W. K. Andrews, 2002. "Higher-Order Improvements of a Computationally Attractive "k"-Step Bootstrap for Extremum Estimators," Econometrica, Econometric Society, vol. 70(1), pages 119-162, January.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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