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.
Original language | English |
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Pages (from-to) | 1-17 |
Number of pages | 17 |
Journal | Journal of Econometrics |
Volume | 190 |
Issue number | 1 |
Early online date | 11 Sept 2015 |
DOIs | |
Publication status | Published - Jan 2016 |