Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions

We propose a data-driven least-square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of conditional cumulative distribution functions and conditional quantile functions. We allow for general multivariate covariates that can be continuous, categorica...

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Bibliographic Details
Published in:Journal of business & economic statistics 2013-01, Vol.31 (1), p.57-65
Main Authors: Li, Qi, Lin, Juan, Racine, Jeffrey S.
Format: Article
Language:English
Subjects:
Asymptotic methods
Childrens health
Continuous variables
Cross-validation
Cumulative distribution functions
Data smoothing
Data-driven
Economic statistics
Economic theory
Estimation methods
Estimators
Kernel functions
Kernel smoothing
Monte Carlo simulation
Parent socioeconomic status
Quantiles
Statistical estimation
Stochastic models
Studies
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Summary:We propose a data-driven least-square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of conditional cumulative distribution functions and conditional quantile functions. We allow for general multivariate covariates that can be continuous, categorical, or a mix of either. We provide asymptotic analysis, examine finite-sample properties via Monte Carlo simulation, and consider an application involving testing for first-order stochastic dominance of children's health conditional on parental education and income. This article has supplementary materials online.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2012.738955