Kim, Hyeongwoo and Durmaz, Nazif (2009): Bias Correction and Out-of-Sample Forecast Accuracy.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Bias Correction and Out-of-Sample Forecast Accuracy|
|Keywords:||Small-Sample Bias; Grid Bootstrap; Recursive Mean Adjustment; Out-of-Sample Forecast; Diebold-Mariano Test|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods|
|Depositing User:||Hyeongwoo Kim|
|Date Deposited:||14. Aug 2009 06:06|
|Last Modified:||12. Feb 2013 00:43|
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