Bias Correction and Out-of-Sample Forecast Accuracy

Hyeongwoo Kim () and Nazif Durmaz

MPRA Paper from University Library of Munich, Germany

Abstract: The least squares (LS) estimator suffers from signicant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best in-sample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their out-of-sample forecast performances with that of the LS estimator. We find that bias-corrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best.

Keywords: Small-Sample Bias; Grid Bootstrap; Recursive Mean Adjustment; Out-of-Sample Forecast; Diebold-Mariano Test (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Date: 2009-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Bias correction and out-of-sample forecast accuracy (2012)
Working Paper: Bias Correction and Out-of-Sample Forecast Accuracy (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:16780

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