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Series estimation under cross-sectional dependence

Listed author(s):
  • Lee, Jungyoon
  • Robinson, Peter M.
Registered author(s):

    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304407615002213
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    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 190 (2016)
    Issue (Month): 1 ()
    Pages: 1-17

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    Handle: RePEc:eee:econom:v:190:y:2016:i:1:p:1-17
    DOI: 10.1016/j.jeconom.2015.08.001
    Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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