Publication Type
Journal Article
Version
publishedVersion
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
Citation
PHILIPS, Peter C.B; SUN, Yixiao; and JIN, Sainan.
Spectral density estimation and robust hypothesis testing using steep origin kernels without truncation. (2006). International Economic Review. 47, (3), 837-894.
Available at: https://ink.library.smu.edu.sg/soe_research/1993
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org./10.1111/j.1468-2354.2006.00398.x