A model sufficiency test using permutation entropy. (17th January 2022)

• Record Type: Journal Article
• Authors: Huang, Xin; Shang, Han Lin; Pitt, David
• Is Part Of: Journal of forecasting. Volume 41:Number 5(2022)
• Page Start: 1017
• Page End: 1036
• Publication Date: 2022-01-17
• Subjects: bivariate dependence -- BDS test -- model evaluation -- ordinal pattern -- prediction accuracy; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2849
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract: Using the ordinal‐pattern concept in permutation entropy, we propose a model sufficiency test to study a given model's point prediction accuracy. Compared with some classical model sufficiency tests, such as Broock et al.'s (1996) test, our proposal does not require a sufficient model to eliminate all structures exhibited in the estimated residuals. When the innovations in the investigated data's underlying dynamics show a certain structure, such as higher moment serial dependence, Broock et al.'s (1996) test can lead to erroneous conclusions about the sufficiency of point predictors. Due to the structured innovations, inconsistency between the model sufficiency tests and prediction accuracy criteria can occur. Our proposal fills in this incoherence between model and prediction evaluation approaches and remains valid when the underlying process has nonwhite additive innovation.