Forecast Evaluation of Small Nested Model Sets

By K. Hubrich, Ken West. Social Science Research Network, 2010

54 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/39aeaa3c07529c4f2ad2361faba764714927d7ef

Citations in the corpus

• Out-of-sample exchange rate predictability with Taylor rule fundamentals (2009, unknown): “Hubrich and West (2007) develop a similar procedure based on the White (2000) test. reported above is not an artifact of picking the output gap specification with the lowest p-value for each model.”
• The Impact of Tax Morale and Institutional Quality on the Shadow Economy (2009, unknown): “The first result is in line with previous findings such as Friedman et al. (2000) and Dreher and Schneider (2006).”