Affiliation:
1. School of Economics University of Surrey Guildford UK
2. School of Social Sciences, School of Economics and Management Tsinghua University Beijing China
3. Department of Economics Rutgers University New Brunswick New Jersey USA
Abstract
SummaryWe develop forecast superiority tests that are robust to the choice of loss function by following Jin, Corradi and Swanson (JCS: 2017), and relying on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid and are correctly sized only under the least favorable case, our tests are uniformly asymptotically valid and non‐conservative. To show this, we establish uniform convergence of HAC variance estimators. Monte Carlo experiments indicate good finite sample performance of our tests, and an empirical illustration suggests that prior forecast accuracy matters in the Survey of Professional Forecasters.
Funder
National Natural Science Foundation of China
Subject
Economics and Econometrics,Social Sciences (miscellaneous)