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Estimation in a semiparametric panel data model with nonstationarity

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  • Chaohua Dong
  • Jiti Gao
  • Bin Peng

Abstract

In this paper, we consider a partially linear panel data model with nonstationarity and certain cross-sectional dependence. Accounting for the explosive feature of the nonstationary time series, we particularly employ Hermite orthogonal functions in this study. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the unknown functions for the cases where N and T go jointly to infinity. Rates of convergence and asymptotic normalities are established for the proposed estimators. Both the finite sample performance and the empirical applications show that the proposed estimation methods work well.

Suggested Citation

  • Chaohua Dong & Jiti Gao & Bin Peng, 2019. "Estimation in a semiparametric panel data model with nonstationarity," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 961-977, September.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:8:p:961-977
    DOI: 10.1080/07474938.2018.1514021
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    Cited by:

    1. Casas Villalba, Maria Isabel & Mao, Xiuping & Lopes Moreira Da Veiga, María Helena, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Gao, Jiti & Xia, Kai & Zhu, Huanjun, 2020. "Heterogeneous panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 219(2), pages 329-353.

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