 Bias correction and out-of-sample forecast accuracyHyeongwoo Kim () and Nazif Durmaz International Journal of Forecasting, 2012, vol. 28, issue 3, 575-586 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. Keywords: Small-sample bias; Relative variance; Grid bootstrap; Recursive mean adjustment; Out-of-sample forecast (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:28:y:2012:i:3:p:575-586 DOI: 10.1016/j.ijforecast.2012.02.009 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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