Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions
AbstractWe 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.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.
Volume (Year): 31 (2013)
Issue (Month): 1 (January)
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Other versions of this item:
- Qi Li & Juan Lin & Jeffrey S. Racine, 2012. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Department of Economics Working Papers 2012-10, McMaster University.
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