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Bias correction and out-of-sample forecast accuracy


  • Kim, Hyeongwoo
  • Durmaz, Nazif


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.

Suggested Citation

  • Kim, Hyeongwoo & Durmaz, Nazif, 2012. "Bias correction and out-of-sample forecast accuracy," International Journal of Forecasting, Elsevier, vol. 28(3), pages 575-586.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:575-586 DOI: 10.1016/j.ijforecast.2012.02.009

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    References listed on IDEAS

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    Cited by:

    1. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    2. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    3. Carlos A. Medel & Pablo M. Pincheira, 2016. "The out-of-sample performance of an exact median-unbiased estimator for the near-unity AR(1) model," Applied Economics Letters, Taylor & Francis Journals, vol. 23(2), pages 126-131, February.
    4. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    5. Gonçalves Mazzeu, Joao Henrique & Ruiz, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item


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

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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