Bruce E. Hansen
"Nonparametric Estimation of Smooth Conditional Distributions"
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.