Bruce E. Hansen

"Nonparametric Estimation of Smooth Conditional Distributions"

May 2004


This paper considers nonparametric estimation of smooth conditional distribution functions (CDFs) using kernel smoothing methods. We propose estimation using a new smoothed local linear (SLL) estimator. Estimation bias is reduced through the use of a local linear estimator rather than local averaging. Estimation variance is reduced through the use of smoothing. Asymptotic analysis of mean integrated squared error (MISE) reveals the form of these efficiency gains, and their magnitudes are demonstrated in numerical simulations. Considerable attention is devoted to the development of a plug-in rule for bandwidths which minimize estimates of the asymptotic MISE. We illustrate the estimation method with an application to the U.S. quarterly GDP growth rate.

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Some of the above material is based upon work supported by the National Science Foundation under Grants No. SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect the views of the NSF.