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Block empirical likelihood for partially linear panel data models with fixed effects

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  • He, Bang-Qiang
  • Hong, Xing-Jian
  • Fan, Guo-Liang

Abstract

In this article, we consider a partially linear panel data models with fixed effects. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to partially linear panel data models with fixed effects, and prove a nonparametric version of Wilks’ theorem which can be used to construct the confidence region for the parametric. By the block empirical likelihood ratio function, the maximum empirical likelihood estimator of the parameter is defined and the asymptotic normality is shown. A simulation study and a real data application are undertaken to assess the finite sample performance of our proposed method.

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  • He, Bang-Qiang & Hong, Xing-Jian & Fan, Guo-Liang, 2017. "Block empirical likelihood for partially linear panel data models with fixed effects," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 128-138.
    • Handle: RePEc:eee:stapro:v:123:y:2017:i:c:p:128-138
    DOI: 10.1016/j.spl.2016.11.021
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    References listed on IDEAS

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    2. Bang-Qiang He & Xing-Jian Hong & Guo-Liang Fan, 2020. "Penalized empirical likelihood for partially linear errors-in-variables panel data models with fixed effects," Statistical Papers, Springer, vol. 61(6), pages 2351-2381, December.

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