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Predicting Tail-Risks for the Italian Economy

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024. "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 339-366, November.
    • Handle: RePEc:spr:jbuscr:v:20:y:2024:i:3:d:10.1007_s41549-025-00106-1
    DOI: 10.1007/s41549-025-00106-1
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    Keywords

    Density forecasts; Tail forecasts; Bayesian VAR; BART; Gaussian Process; Debt; Deficit; Italy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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