The University of Southampton
University of Southampton Institutional Repository

Jackknife estimation with a unit root

Jackknife estimation with a unit root
Jackknife estimation with a unit root
We study jackknife estimators in a first-order autoregression with a unit root. Non-overlapping sub-sample estimators have different limit distributions, so the jackknife does not fully eliminate first-order bias. We therefore derive explicit limit distributions of the numerator and denominator to calculate the expectations that determine optimal jackknife weights. Simulations show that the resulting jackknife estimator produces substantial reductions in bias and RMSE
0167-7152
1677-1682
Chambers, Marcus J.
6591c606-5ed7-409f-a741-77d1a13e9c39
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7
Chambers, Marcus J.
6591c606-5ed7-409f-a741-77d1a13e9c39
Kyriacou, Maria
6234587e-81f1-4e1d-941d-395996f8bda7

Chambers, Marcus J. and Kyriacou, Maria (2013) Jackknife estimation with a unit root. Statistics & Probability Letters, 83 (7), 1677-1682. (doi:10.1016/j.spl.2013.03.016).

Record type: Article

Abstract

We study jackknife estimators in a first-order autoregression with a unit root. Non-overlapping sub-sample estimators have different limit distributions, so the jackknife does not fully eliminate first-order bias. We therefore derive explicit limit distributions of the numerator and denominator to calculate the expectations that determine optimal jackknife weights. Simulations show that the resulting jackknife estimator produces substantial reductions in bias and RMSE

Text
pii/S0167715213000990 - Other
Download (45kB)

More information

Published date: July 2013
Organisations: Economics

Identifiers

Local EPrints ID: 351304
URI: https://eprints.soton.ac.uk/id/eprint/351304
ISSN: 0167-7152
PURE UUID: 6259e3b4-3f16-4810-94cd-4b9e6a51bd46

Catalogue record

Date deposited: 19 Apr 2013 08:44
Last modified: 19 Jul 2019 21:38

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×