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Jackknife Estimation of Stationary Autoregressive Models

Chambers, MJ (2010) Jackknife Estimation of Stationary Autoregressive Models. UNSPECIFIED. University of Essex, Department of Economics, Economics Discussion Papers 684, Colchester.

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Abstract

This paper reports the results of an extensive investigation into the use of the jackknife as a method of estimation in stationary autoregressive models. In addition to providing some general theoretical results concerning jackknife methods it is shown that a method based on the use of non-overlapping sub-intervals is found to work particularly well and is capable of reducing bias and root mean squared error (RMSE) compared to ordinary least squares (OLS), subject to a suitable choice of the number of sub-samples, rules-of-thumb for which are provided. The jackknife estimators also outperform OLS when the distribution of the disturbances departs from normality and when it is subject to autoregressive conditional heteroskedasticity. Furthermore the jackknife estimators are much closer to being median-unbiased than their OLS counterparts.

Item Type: Monograph (UNSPECIFIED)
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics, Department of
Depositing User: Jim Jamieson
Date Deposited: 04 Jul 2012 22:09
Last Modified: 17 Aug 2017 18:11
URI: http://repository.essex.ac.uk/id/eprint/2786

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