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Title: A novel approach to predictive accuracy testing in nested environments Authors:  Jean-Yves Pitarakis - University of Southampton (United Kingdom) [presenting]
Abstract: A new approach is introduced to compare the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of loss differentials used in the construction of commonly used predictive comparison statistics in the literature. The approach continues to rely on the out of sample MSE loss differentials between the two competing models, leads to Gaussian asymptotics and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g. stationary and local to unit root). Simulations indicate that our methods have good finite sample properties.