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.
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References
Feynman, R., Leighton, R., Sands, M.: The Feynman Lectures on Physics. Addison Wesley, Boston (2005)
Li, M., Vitányi, P.M.B.: An Introduction to Kolmogorov Complexity and Its Applications. Springer, Berlin (2008)
Shmueli, G.: To explain or to predict? Stat. Sci. 25(3), 289–310 (2010)
Acknowledgments
This work was supported by the Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand. We also acknowledge the partial support of Department of Mathematics, Chiang Mai University, and of the US National Science Foundation via grant HRD-1242122 (Cyber-ShARE Center of Excellence).
The authors are greatly thankful to Professors Hung T. Nguyen and Galit Shmueli for valuable discussions.
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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
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DOI: https://doi.org/10.1007/978-3-030-04263-9_12
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