Cross-validated mixed-datatype bandwidth selection for nonparametric cumulative distribution/survivor functions

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  • Cong Li, Shanghai Univ Finance & Econ, Shanghai University of Finance & Economics, Sch Econ
  • ,
  • Hongjun Li, Capital Univ Econ & Business, Capital University of Economics & Business, Int Sch Econ & Management
  • ,
  • Jeffrey S. Racine

We propose a computationally efficient data-driven least square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of cumulative distribution/survivor functions. We allow for general multivariate covariates that can be continuous, discrete/ordered categorical or a mix of either. We provide asymptotic analysis, examine finite-sample properties through Monte Carlo simulation, and consider an illustration involving nonparametric copula modeling. We also demonstrate how the approach can also be used to construct a smooth Kolmogorov-Smirnov test that has a slightly better power profile than its nonsmooth counterpart.

Original languageEnglish
JournalEconometric Reviews
Pages (from-to)970-987
Number of pages18
Publication statusPublished - 2017

    Research areas

  • Bandwidth selection, Kolmogorov-Smirnov test, least square cross-validation, mixed-data, QUANTILE FUNCTIONS, MODELS

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