Forecast evaluation of small nested model sets

Kirstin Hubrich () and Kenneth West ()

No 1030, Working Paper Series from European Central Bank

Abstract: We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the bench-mark to all the alternative models simultaneously rather than sequentially, and do not require re-estimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different models for U.S. inflation. JEL Classification: C32, C53, E37

Keywords: inflation forecasting; multiple model comparisons; out-of-sample; prediction; testing (search for similar items in EconPapers)
Date: 2009-03
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
Note: 1325881
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Forecast evaluation of small nested model sets (2010)
Working Paper: Forecast Evaluation of Small Nested Model Sets (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20091030

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