Abstract
In 2018 a process change in the industry sector initiated at the “center” (West Europe) induced internal spillover effects at the end of the value chain (Romania) and propagated further affecting the “center” with a lag. Classical econometrics deals poorly with circular change. Support Vector Machines (SVM) can deal with that. As it can quantify small movements in “animal spirit” (coming from survey data) as novelties, that way it can monitor equilibrium changes (location shifts) ahead of hard indicators (industrial production index). This is a positive side of “animal spirit.” Confronted with a Rare Event, an unexpected change like that of the Virus Crisis in 2020, the same survey data produce outliers. “Animal spirit” shows its negative side as irrational behavior.
With support from National Bank of Romania; the usual disclaimer applies. The author would like to thank reviewers and participants at the 6th International Conference on Machine Learning, Optimization, and Data Science LOD 2020 for useful comments.
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Code source adapted from scikit-learn. https://scikit-learn.org/. Accessed 13 May 2020
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Igescu, I. (2020). The Role of Animal Spirit in Monitoring Location Shifts with SVM:Novelties Versus Outliers. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_8
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DOI: https://doi.org/10.1007/978-3-030-64583-0_8
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