Title

Semiparametric Estimator of Time Series Conditional Variance

Publication Type

Journal Article

Publication Date

2-2010

Abstract

We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecification our estimator can still be consistent. It also improves over the nonparametric estimator of Ziegelmann (2002) in terms of bias reduction. The superiority of our estimator is verfied by Monte Carlo simulations and empirical data analysis.

Keywords

Conditional variance; Nonparametric estimator; Semiparametric models

Discipline

Economics

Research Areas

Econometrics

Publication

Journal of Business and Economic Statistics

Volume

28

Issue

2

First Page

256

Last Page

274

ISSN

0735-0015

Identifier

10.1198/jbes.2009.08118

Publisher

Taylor and Francis

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1198/jbes.2009.08118

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