Publication Type

Journal Article

Publication Date

8-2006

Abstract

A new class of kernels for long-run variance and spectral density estimation is developed by exponentiating traditional quadratic kernels. Depending on whether the exponent parameter is allowed to grow with the sample size, we establish different asymptotic approximations to the sampling distribution of the proposed estimators. When the exponent is passed to infinity with the sample size, the new estimator is consistent and shown to be asymptotically normal. When the exponent is fixed, the new estimator is inconsistent and has a nonstandard limiting distribution. It is shown via Monte Carlo experiments that, when the chosen exponent is small in practical applications, the nonstandard limit theory provides better approximations to the finite sample distributions of the spectral density estimator and the associated test statistic in regression settings.

Discipline

Econometrics

Research Areas

Econometrics

Publication

International Economic Review

Volume

47

Issue

3

First Page

837

Last Page

894

ISSN

0020-6598

Identifier

10.1111/j.1468-2354.2006.00398.x

Publisher

Wiley: 24 months

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://doi.org./10.1111/j.1468-2354.2006.00398.x

Included in

Econometrics Commons

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