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Nested forecast model comparisons: A new approach to testing equal accuracy

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  • Clark, Todd E.
  • McCracken, Michael W.

Abstract

We develop methods for testing whether, in a finite sample, forecasts from nested models are equally accurate. Most prior work has focused on a null of equal accuracy in population — basically, whether the additional coefficients of the larger model are zero. Our asymptotic approximation instead treats the coefficients as non-zero but small, such that, in a finite sample, forecasts from the small and large models are expected to be equally accurate. We derive the limiting distributions of tests of equal mean square error, and develop a bootstrap for inference. Simulations show that our procedures have good size and power properties.

Suggested Citation

  • Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
  • Handle: RePEc:eee:econom:v:186:y:2015:i:1:p:160-177
    DOI: 10.1016/j.jeconom.2014.06.016
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    More about this item

    Keywords

    Mean square error; Prediction;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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