Series estimation under cross-sectional dependence

Jungyoon Lee, Peter M Robinson

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalJournal of Econometrics
Issue number1
Early online date11 Sept 2015
Publication statusPublished - Jan 2016

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