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

Todd Clark and Michael McCracken

Journal of Econometrics, 2015, vol. 186, issue 1, 160-177

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.

Keywords: Mean square error; Prediction (search for similar items in EconPapers)
JEL-codes: C12 C52 C53 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)

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Related works:
Working Paper: Nested forecast model comparisons: a new approach to testing equal accuracy (2009)
Working Paper: Nested forecast model comparisons: a new approach to testing equal accuracy (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:186:y:2015:i:1:p:160-177

DOI: 10.1016/j.jeconom.2014.06.016

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