Nonparametric estimation of ratios of noise to signal in stochastic regression

Howell Tong and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: In this paper, we study three different types of estimates for the noise-to signal ratios in a general stochastic regression setup. The locally linear and locally quadratic regression estimators serve as the building blocks in our approach. Under the assumption that the observations are strictly stationary and absolutely regular, we establish the asymptotic normality of the estimates, which indicates that the residual-based estimates are to be preferred. Further, the locally quadratic regression reduces the bias when compared with the locally linear (or locally constant) regression without the concomitant increase in the asymptotic variance, if the same bandwidth is used. The asymptotic theory also paves the way for a fully data-driven under smoothing scheme to reduce the biases in estimation. Numerical examples with both simulated and real data sets are used as illustration.

Keywords: absolutely regular; asymptotic normality; local polynomial regression; noise to signal ratio. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2000-07
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Published in Statistica Sinica, July, 2000, 10(3), pp. 751-770. ISSN: 1017-0405

Downloads: (external link)
http://eprints.lse.ac.uk/6324/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:6324

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().