Možnosti iskanja
Domov Mediji Pojasnjujemo Raziskave in publikacije Statistika Denarna politika Euro Plačila in trgi Zaposlitve
Predlogi
Razvrsti po
Ni na voljo v slovenščini.

Markus Holopainen

2 May 2016
WORKING PAPER SERIES - No. 1900
Details
Abstract
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground. Generally, our results show that the conventional statistical approaches are outperformed by more advanced machine learning methods, such as k-nearest neighbors and neural networks, and particularly by model aggregation approaches through ensemble learning.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
F30 : International Economics→International Finance→General
G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation

To spletno mesto uporablja piškotke

Funkcionalne piškotke uporabljamo za shranjevanje nastavitev uporabnikov in analitične piškotke za izboljšanje učinkovitosti delovanja spletnega mesta. Uporabljamo tudi piškotke tretjih oseb, nameščene s storitvami tretjih oseb, ki so vključene v spletno mesto. Piškotke lahko sprejmete ali zavrnete. Če želite več informacij ali spremeniti izbiro piškotkov in strežniških dnevnikov, ki jih uporabljamo, si poglejte naslednje: