A predictability test for a small number of nested models

Eleonora Granziera, Kirstin Hubrich, Hyungsik Roger Moon

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

We introduce quasi-likelihood ratio tests for one sided multivariate hypotheses to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. The limiting distributions of the test statistics are non-standard. For critical values we consider: (i) bootstrapping and (ii) simulations assuming normality of the mean square prediction error difference. The proposed tests have good size and power properties compared with existing equal and superior predictive ability tests for multiple model comparison. We apply our tests to study the predictive ability of a Phillips curve type for the US core inflation.

Original languageEnglish
Pages (from-to)174-185
Number of pages12
JournalJournal of Econometrics
Volume182
Issue number1
DOIs
Publication statusPublished - 2014 Sep

Bibliographical note

Funding Information:
Roger Moon acknowledges the financial support of Yonsei University ( 2013220003 ). We thank Raffaella Giacomini, Peter Hansen, Søren Johansen, Michael McCracken, Hashem Pesaran, Norman Swanson, Kenneth West, participants at USC, BoC, Conference in Honor of Hal White, NASM 2011, AMES 2011, ESEM 2011 and EUI Conference, two anonymous referees for helpful comments and suggestions. The views expressed are those of the authors and do not represent those of the BoC or ECB.

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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