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Bias correction and out-of-sample forecast accuracy

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  • Kim, Hyeongwoo
  • Durmaz, Nazif

Abstract

We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in enhancing the out-of-sample forecast accuracy. We employ 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 increases the variance of the estimator. Both the bias and the relative variance tend to decrease as the sample size (T) increases, meaning that larger numbers of observations do not always imply gains from bias-correction. As the degree of persistence increases, the bias becomes greater while the relative variance becomes smaller, which implies a greater gain from correcting for bias for highly persistent data. We also provide real data applications that confirm our major findings overall.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 28 (2012)
Issue (Month): 3 ()
Pages: 575-586

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Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:575-586

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Small-sample bias; Relative variance; Grid bootstrap; Recursive mean adjustment; Out-of-sample forecast;

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  7. 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.
  8. Jon Steinsson, 2005. "The Dynamic Behavior of the Real Exchange Rate in Sticky Price Models," Economics wp28_jonst, Department of Economics, Central bank of Iceland.
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