Optimal bandwidth selection for nonparametric conditional distribution and quantile functions

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Optimal bandwidth selection for nonparametric conditional distribution and quantile functions. / Li, Qi; Lin, J.; Racine, J.S.

In: Journal of Business and Economic Statistics, Vol. 31, No. 1, 01.01.2013, p. 57-65.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Vancouver

Li Q, Lin J, Racine JS. Optimal bandwidth selection for nonparametric conditional distribution and quantile functions. Journal of Business and Economic Statistics. 2013 Jan 1;31(1):57-65. doi: 10.1080/07350015.2012.738955

Author

Li, Qi ; Lin, J. ; Racine, J.S. / Optimal bandwidth selection for nonparametric conditional distribution and quantile functions. In: Journal of Business and Economic Statistics. 2013 ; Vol. 31, No. 1. pp. 57-65.

Bibtex

@article{6c80d2e64ecd40ff82cdf4b13c1142f9,

title = "Optimal bandwidth selection for nonparametric conditional distribution and quantile functions",

abstract = "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.",

author = "Qi Li and J. Lin and J.S. Racine",

year = "2013",

month = jan,

day = "1",

doi = "10.1080/07350015.2012.738955",

language = "English",

volume = "31",

pages = "57--65",

journal = "Journal of Business and Economic Statistics",

issn = "0735-0015",

publisher = "Taylor & Francis Inc.",

number = "1",

}

RIS

TY - JOUR

T1 - Optimal bandwidth selection for nonparametric conditional distribution and quantile functions

AU - Li, Qi

AU - Lin, J.

AU - Racine, J.S.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84876090819&partnerID=8YFLogxK

U2 - 10.1080/07350015.2012.738955

DO - 10.1080/07350015.2012.738955

M3 - Journal article

AN - SCOPUS:84876090819

VL - 31

SP - 57

EP - 65

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 1

ER -

Department of Economics and Business Economics