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Testing for Trend Specifications in Panel Data Models

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  • Jilin Wu
  • Xiaojun Song
  • Zhijie Xiao

Abstract

This article proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroscedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power against two classes of local alternatives approaching the null at different rates. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test implemented with bootstrap p-values performs reasonably well in finite samples. Finally, an empirical application to the analysis of the U.S. per capita personal income trend highlights the usefulness of our test in real datasets.

Suggested Citation

  • Jilin Wu & Xiaojun Song & Zhijie Xiao, 2023. "Testing for Trend Specifications in Panel Data Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 453-466, April.
    • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:2:p:453-466
    DOI: 10.1080/07350015.2022.2035227
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    Cited by:

    1. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

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