Why the Best Predictive Models Are Often Different from the Best Explanatory Models: A Theoretical Explanation

Authors

Songsak Sriboonchitta, Chiang Mai UniversityFollow
Luc Longpre, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow
Thongchai Dumrongpokaphan, Chiang Mai UniversityFollow

Publication Date

5-2018

Comments

Technical Report: UTEP-CS-18-44

Sriboonchitta S., Longpré L., Kreinovich V., Dumrongpokaphan T. (2019) Why the Best Predictive Models Are Often Different from the Best Explanatory Models: A Theoretical Explanation. In: Kreinovich V., Sriboonchitta S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham
https://doi.org/10.1007/978-3-030-04263-9_12

Abstract

Traditionally, in statistics, it was implicitly assumed that models which are the best predictors also have the best explanatory power. Lately, many examples have been provided that show that the best predictive models are often different from the best explanatory models. In this paper, we provide a theoretical explanation for this difference.