Nonparametric tests for Optimal Predictive Ability

Stelios Arvanitis, Thierry Post, Valerio Potì, Selcuk Karabati

Research output: Contribution to journalArticlepeer-review

Abstract

A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of Optimal Predictive Ability generalizes the Superior Predictive Ability hypothesis from a single given loss function to an entire class of loss functions. Distinction is drawn between General Loss functions, Convex Loss functions, and Symmetric Convex Loss functions. The research hypothesis is formulated in terms of moment inequality conditions. The empirical moment conditions are reduced to an exact and finite system of linear inequalities based on piecewise-linear loss functions. The hypothesis can be tested in a statistically consistent way using a blockwise Empirical Likelihood Ratio test statistic. A computationally feasible test procedure computes the test statistic using Convex Optimization methods, and estimates conservative, data-dependent critical values using a majorizing chi-square limit distribution and a moment selection method. An empirical application to inflation forecasting reveals that a very large majority of thousands of forecast models are redundant, leaving predominantly Phillips Curve-type models, when convexity and symmetry are assumed.

Original languageEnglish
JournalInternational Journal of Forecasting
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Empirical likelihood
  • Forecast comparison
  • Inflation forecasting
  • Moment selection
  • Stochastic Dominance

ASJC Scopus subject areas

  • Business and International Management

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