Publication Date



Technical Report: UTEP-CS-18-42

Dumrongpokaphan T., Kreinovich V., Sriboonchitta S. (2019) Why Threshold Models: A Theoretical Explanation. In: Kreinovich V., Thach N., Trung N., Van Thanh D. (eds) Beyond Traditional Probabilistic Methods in Economics. ECONVN 2019. Studies in Computational Intelligence, vol 809. Springer, Cham


Many economic phenomena are well described by linear models. In such models, the predicted value of the desired quantity -- e.g., the future value of an economic characteristic -- linearly depends on the current values of this and related economic characteristic and on the numerical values of external effects. Linear models have a clear economic interpretation: they correspond to situations when the overall effect does not depend, e.g., on whether we consider a loose federation as a single country or as several countries. While linear models are often reasonably accurate, to get more accurate predictions, we need to take into account that real-life processes are nonlinear. To take this nonlinearity into account, economists use piece-wise linear (threshold) models, in which we have several different linear dependencies in different domains. Surprisingly, such piece-wise linear models often work better than more traditional models of non-linearity -- e.g., models that take quadratic terms into account. In this paper, we provide a theoretical explanation for this empirical success of threshold models.