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Higher-order Expansions and Inference for Panel Data Models

Author

Listed:
  • Jiti Gao
  • Bin Peng
  • Yayi Yan

Abstract

In this paper, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and crosssectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitly investigating a panel data model with interactive effects which nests many traditional panel data models as special cases. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.

Suggested Citation

  • Jiti Gao & Bin Peng & Yayi Yan, 2023. "Higher-order Expansions and Inference for Panel Data Models," Monash Econometrics and Business Statistics Working Papers 14/23, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2023-14
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp14-2023.pdf
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    More about this item

    Keywords

    dependent wild bootstrap; Edgeworth expansion; fund performance evaluation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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