Monash University
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Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence

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posted on 2022-11-09, 00:26 authored by Bin Peng, Chaohua Dong, Jiti Gao
In this paper, we consider a semiparametric single index panel data mode with cross-sectional dependence, high-dimensionality and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and a link function for the case where both N and T go to ∞. Rates of convergence and asymptotic normality consistencies are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.

History

Classification-JEL

C13, C14, C23

Creation date

2014-02-01

Working Paper Series Number

9/14

Length

39

File-Format

application/pdf

Handle

RePEc:msh:ebswps:2014-9

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