Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions
AbstractWe propose a data-driven least squares 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.
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Bibliographic InfoPaper provided by McMaster University in its series Department of Economics Working Papers with number 2012-10.
Length: 37 pages
Date of creation: Oct 2012
Date of revision:
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Other versions of this item:
- Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
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