Series estimation under cross-sectional dependence

Jungyoon Lee and Peter M. Robinson

Journal of Econometrics, 2016, vol. 190, issue 1, 1-17

Abstract: An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic applications, and can apply also to panel, spatial and time series data. A mean square rate of convergence of nonparametric regression estimates is established followed by asymptotic normality of a quite general statistic. Data-driven studentizations that rely on single or double indices to order the data are justified. In a partially linear model setting, Monte Carlo investigation of finite sample properties and two empirical applications are carried out.

Keywords: Series estimation; Nonparametric regression; Semiparametric regression; Spatial data; Cross-sectional dependence; Mean square rate of convergence; Functional central limit theorem; Data-driven studentization (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C21 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:190:y:2016:i:1:p:1-17

DOI: 10.1016/j.jeconom.2015.08.001

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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