Predicting Tail-Risks for the Italian Economy

Maximilian Boeck (), Massimiliano Marcellino (), Michael Pfarrhofer () and Tommaso Tornese ()
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Maximilian Boeck: Friedrich-Alexander-University Erlangen-Nuremberg
Massimiliano Marcellino: Bocconi University
Michael Pfarrhofer: Vienna University of Economics and Business
Tommaso Tornese: Università Cattolica di Milano

Journal of Business Cycle Research, 2024, vol. 20, issue 3, No 1, 339-366

Abstract: 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%.

Keywords: Density forecasts; Tail forecasts; Bayesian VAR; BART; Gaussian Process; Debt; Deficit; Italy (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s41549-025-00106-1

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