Bootstrap, Jackknife and COLS: Bias and Mean Squared Error in Estimation of Autoregressive Models

Bootstrap, Jackknife and COLS: Bias and Mean Squared Error in Estimation of Autoregressive Models Abstract We compare a number of bias-correction methodologies in terms of mean squared error and remaining bias, including the residual bootstrap, the relatively unexplored Quenouille jackknife, and methods based on analytical approximation of moments. We introduce a new higher-order jackknife estimator for the AR(1) with constant. Simulation results are presented for four different error structures, including GARCH. We include results for a relatively extreme situation where the errors are highly skewed and leptokurtic. It is argued that the bootstrap and analytical-correction (COLS) approaches are to be favoured overall, though the jackknife methods are the least biased. We find that COLS tends to have the lowest mean squared error, though the bootstrap also does well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Time Series Econometrics de Gruyter

Bootstrap, Jackknife and COLS: Bias and Mean Squared Error in Estimation of Autoregressive Models

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Publisher
de Gruyter
Copyright
Copyright © 2012 by the
eISSN
1941-1928
DOI
10.1515/1941-1928.1122
Publisher site
See Article on Publisher Site

Abstract

Abstract We compare a number of bias-correction methodologies in terms of mean squared error and remaining bias, including the residual bootstrap, the relatively unexplored Quenouille jackknife, and methods based on analytical approximation of moments. We introduce a new higher-order jackknife estimator for the AR(1) with constant. Simulation results are presented for four different error structures, including GARCH. We include results for a relatively extreme situation where the errors are highly skewed and leptokurtic. It is argued that the bootstrap and analytical-correction (COLS) approaches are to be favoured overall, though the jackknife methods are the least biased. We find that COLS tends to have the lowest mean squared error, though the bootstrap also does well.

Journal

Journal of Time Series Econometricsde Gruyter

Published: Nov 14, 2012

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