Library Header Image
LSE Research Online LSE Library Services

Prediction and nonparametric estimation for time series with heavy tails

Hall, Peter, Peng, Liang and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2002) Prediction and nonparametric estimation for time series with heavy tails. Journal of Time Series Analysis, 23 (3). pp. 313-331. ISSN 0143-9782

Download (333kB) | Preview

Identification Number: 10.1111/1467-9892.00266


Motivated by prediction problems for time series with heavy-tailed marginal distributions, we consider methods based on `local least absolute deviations' for estimating a regression median from dependent data. Unlike more conventional `local median' methods, which are in effect based on locally fitting a polynomial of degree 0, techniques founded on local least absolute deviations have quadratic bias right up to the boundary of the design interval. Also in contrast to local least-squares methods based on linear fits, the order of magnitude of variance does not depend on tail-weight of the error distribution. To make these points clear, we develop theory describing local applications to time series of both least-squares and least-absolute-deviations methods, showing for example that, in the case of heavy-tailed data, the conventional local-linear least-squares estimator suffers from an additional bias term as well as increased variance.

Item Type: Article
Official URL:
Additional Information: ® 2002 Blackwell Publishing
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 26 Jun 2008 10:13
Last Modified: 20 Oct 2021 00:41

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics