Forecast Evaluation of Small Nested Model Sets

Kirstin Hubrich · Kenneth D. West

75 citations0/0 files storedJournal of Applied Econometrics

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Financial Risk and Volatility Modeling

Abstract

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 benchmark 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, while 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 the most accurate size, and the procedure that looks at the maximum t‐statistic has the best power. We illustrate our procedures by comparing forecasts of different models for US inflation. Copyright © 2010 John Wiley & Sons, Ltd.

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1994Comparing Predictive Accuracy 2006Approximately normal tests for equal predictive accuracy in nested models 2000Forecasting Economic Time Series 2007Why Has U.S. Inflation Become Harder to Forecast?2006Tests of Conditional Predictive Ability 1996Asymptotic Inference about Predictive Ability 2005A Test for Superior Predictive Ability 2001Tests of equal forecast accuracy and encompassing for nested models

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