Nonparametric tests for Optimal Predictive Ability
Arvanitis S. Post T. Potì V. Karabati S.
1 April 2021Elsevier B.V.
International Journal of Forecasting
2021#37Issue 2881 - 898 pp.
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.
Empirical likelihood , Forecast comparison , Inflation forecasting , Moment selection , Stochastic Dominance
Text of the article Перейти на текст статьи
Department of Economics of Athens University of Economics and Business, Athens, Greece
Graduate School of Business of Nazarbayev University and Director at the National Analytical Center ‘Analytica’, Astana, Kazakhstan
Michael Smurfit Graduate Business School of the University College Dublin, Dublin, Ireland
Koç University, Sarıyer/Istanbul, 34450, Turkey
Department of Economics of Athens University of Economics and Business
Graduate School of Business of Nazarbayev University and Director at the National Analytical Center ‘Analytica’
Michael Smurfit Graduate Business School of the University College Dublin
Koç University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026