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Bias Correction and Out-of-Sample Forecast Accuracy

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  • Hyeongwoo Kim
  • Nazif Durmaz

Abstract

We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in terms of out-of-sample forecast accuracy, employing two popular methods proposed by Hansen (1999) and So and Shin (1999). Our Monte Carlo simulations show that these methods do not necessarily achieve better forecasting performances than the bias-uncorrected Least Squares (LS) method, because bias correction tends to increase the variance of the estimator. There is a gain from correcting for bias only when the true data generating process is sufficiently persistent. Though the bias arises in finite samples, the sample size (N) is not a crucial factor of the gains from bias-correction, because both the bias and the variance tend to decrease as N goes up. We also provide a real data application with 7 commodity price indices which confirms our findings.

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Paper provided by Department of Economics, Auburn University in its series Auburn Economics Working Paper Series with number auwp2010-02.

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Date of creation: May 2010
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Handle: RePEc:abn:wpaper:auwp2010-02

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Keywords: Small-Sample Bias; Grid Bootstrap; Recursive Mean Adjustment; Out-of-Sample Forecast;

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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. So, Beong Soo & Shin, Dong Wan, 1999. "Recursive mean adjustment in time-series inferences," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 65-73, May.
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  7. Menelaos Karananos & S.H Sekioua & N Zeng, 2005. "On the order of integration of monthly US ex-ante and ex-post real interest rates new evidence from over a century of data," Money Macro and Finance (MMF) Research Group Conference 2005 21, Money Macro and Finance Research Group.
  8. Kim, Hyeongwoo & Moh, Young-Kyu, 2012. "Examining the evidence of purchasing power parity by recursive mean adjustment," Economic Modelling, Elsevier, vol. 29(5), pages 1850-1857.
  9. Kim, Hyeongwoo, 2009. "On the usefulness of the contrarian strategy across national stock markets: A grid bootstrap analysis," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 734-744, December.
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  18. Taylor, A M Robert, 2002. "Regression-Based Unit Root Tests with Recursive Mean Adjustment for Seasonal and Nonseasonal Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 269-81, April.
  19. Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
  20. Kim, Hyeongwoo & Stern, Liliana V. & Stern, Michael L., 2010. "Half-life bias correction and the G7 stock markets," Economics Letters, Elsevier, vol. 109(1), pages 1-3, October.
  21. Cook, Steven, 2002. "Correcting size distortion of the Dickey-Fuller test via recursive mean adjustment," Statistics & Probability Letters, Elsevier, vol. 60(1), pages 75-79, November.
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  23. Andrews, Donald W K & Chen, Hong-Yuan, 1994. "Approximately Median-Unbiased Estimation of Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 187-204, April.
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