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

Bin Peng (), Chaohua Dong () and Jiti Gao

No 9/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: symptotic theory; closed-form estimate; nonlinear panel data model; orthogonal series method (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 (search for similar items in EconPapers)
Pages: 39
Date: 2014
New Economics Papers: this item is included in nep-ecm
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Journal Article: Semiparametric single-index panel data models with cross-sectional dependence (2015)
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