Semiparametric single-index panel data models with cross-sectional dependence

Chaohua Dong, Jiti Gao and Bin Peng ()

Journal of Econometrics, 2015, vol. 188, issue 1, 301-312

Abstract: In this paper, we consider a semiparametric single-index panel data model with cross-sectional dependence 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 the link function for the case where both cross-sectional dimension (N) and temporal dimension (T) go to infinity. Rates of convergence and asymptotic normality 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: Asymptotic theory; Closed-form estimation; Nonlinear panel data model; Orthogonal series expansion method (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 C51 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Working Paper: Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:188:y:2015:i:1:p:301-312

DOI: 10.1016/j.jeconom.2015.06.001

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