Predicting Tail-Risks for the Italian Economy

Böck M, Marcellino M, Pfarrhofer M, Tornese T (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Journal of Business Cycle Research Springer

Article Number: 104493

DOI: 10.1007/s41549-025-00106-1

Abstract

This paper investigates the empirical performance of various econometric methods to predict tail risks for the Italian economy. It provides an overview of recent econometric methods for assessing tail risks, including Bayesian VARs with stochastic volatility (BVAR-SV), Bayesian additive regression trees (BART) and Gaussian processes (GP). In an out-of-sample forecasting exercise for the Italian economy, the paper assesses the point, density, and tail predictive performance for GDP growth, inflation, debt-to-GDP, and deficit-to-GDP ratios. It turns out that BVAR-SV performs particularly well for Italy, in particular for the tails. It is then used to also predict expected shortfalls and longrises for the variables of interest, and the probability of specific interesting events, such as negative growth, inflation above the 2% target, an increase in the debt-to-GDP ratio, or a deficit-to-GDP ratio above 3%.

Authors with CRIS profile

Maximilian Böck Lehrstuhl für Statistik und Ökonometrie

Involved external institutions

Università Commerciale Luigi Bocconi IT Italy (IT) Wirtschaftsuniversität Wien (WU) AT Austria (AT) Catholic University of the Sacred Heart / Università Cattolica del Sacro Cuore IT Italy (IT)

How to cite

APA:

Böck, M., Marcellino, M., Pfarrhofer, M., & Tornese, T. (2025). Predicting Tail-Risks for the Italian Economy. Journal of Business Cycle Research. https://doi.org/10.1007/s41549-025-00106-1

MLA:

Böck, Maximilian, et al. "Predicting Tail-Risks for the Italian Economy." Journal of Business Cycle Research (2025).

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