Publication Type

Journal Article

Version

Preprint

Publication Date

10-2011

Abstract

This article proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local-level nonparametric regression applied to squared residuals of the mean regression. The estimator is shown to be asymptotically equivalent to the local linear estimator in the case of unbounded support but, unlike that estimator, is restricted to be nonnegative in finite samples. It is fully adaptive to the unknown conditional mean function. Simulations are conducted to evaluate the finite-sample performance of the estimator. Two empirical applications are reported. One uses cross-sectional data and studies the relationship between occupational prestige and income, and the other uses time series data on Treasury bill rates to fit the total volatility function in a continuous-time jump diffusion model.

Keywords

Conditional heteroscedasticity, Conditional variance function, Empirical likelihood, Heteroscedastic nonparametric regression, Jump diffusion, Local linear estimator

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Business and Economic Statistics

Volume

29

Issue

4

First Page

518

Last Page

528

ISSN

0735-0015

Identifier

10.1198/jbes.2011.09012

Publisher

Taylor and Francis

Embargo Period

8-5-2017

Additional URL

http://doi.org/10.1198/jbes.2011.09012

Included in

Econometrics Commons

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