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More efficient local polynomial regression with random-effects panel data models

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  • Ke Yang

Abstract

We propose a modification on the local polynomial estimation procedure to account for the “within-subject” correlation presented in panel data. The proposed procedure is rather simple to compute and has a closed-form expression. We study the asymptotic bias and variance of the proposed procedure and show that it outperforms the working independence estimator uniformly up to the first order. Simulation study shows that the gains in efficiency with the proposed method in the presence of “within-subject” correlation can be significant in small samples. For illustration purposes, the procedure is applied to explore the impact of market concentration on airfare.

Suggested Citation

  • Ke Yang, 2018. "More efficient local polynomial regression with random-effects panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 37(7), pages 760-776, August.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:7:p:760-776
    DOI: 10.1080/07474938.2016.1167813
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    Cited by:

    1. Fan Zhang & Jianmu Ye & Chao Zhang & Zhuang Xiong, 2019. "Evolutionary mechanism for subregional cooperation: Evidence from provincial subregion in Central China," Progress in Development Studies, , vol. 19(4), pages 304-326, October.

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