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Bias Correction and Out-of-Sample Forecast Accuracy

  • Kim, Hyeongwoo
  • Durmaz, Nazif

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16780.

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Date of creation: May 2009
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Handle: RePEc:pra:mprapa:16780
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