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The time-varying risk of Italian GDP

Author

Listed:
  • Fabio Busetti

    (Bank of Italy)

  • Michele Caivano

    (Bank of Italy)

  • Davide Delle Monache

    (Bank of Italy)

  • Claudia Pacella

    (Bank of Italy)

Abstract

The uncertainty surrounding economic forecasts is generally related to multiple sources of risks, of domestic and foreign origin. This paper studies the predictive distribution of Italian GDP growth as a function of selected risk indicators, related to both financial and real economic developments. The conditional distribution is characterized by means of expectile regressions. Expectiles are closely related to the Expected Shortfall, a well-known measure of risk with desirable properties. Here a decomposition of Expected Shortfall in terms of contributions of different indicators is proposed, which allows to track over time the main drivers of risk. Our analysis of the predictive distribution of GDP confirms that financial conditions are relevant for the left tail of the distribution but it also highlights that indicators of global trade and uncertainty have strong explanatory power for both left and right tail. Their usefulness is supported also in a pseudo real-time predictive context. Overall, our findings suggest that Italian GDP risks have been mostly driven by foreign developments around the Great Recession, by domestic financial conditions at the time of the Sovereign Debt Crisis and by economic policy uncertainty in more recent years.

Suggested Citation

  • Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_1288_20
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    References listed on IDEAS

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    Cited by:

    1. Ruiz Ortega, Esther & Rodríguez Caballero, Carlos Vladimir & Gonzalez Rivera, Gloria, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. J. David López-Salido & Francesca Loria, 2020. "Inflation at Risk," Finance and Economics Discussion Series 2020-013, Board of Governors of the Federal Reserve System (U.S.).
    3. Gu, Xin & Cheng, Xiang & Zhu, Zixiang & Deng, Xiang, 2021. "Economic policy uncertainty and China’s growth-at-risk," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 452-467.

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    More about this item

    Keywords

    asymmetric least squares; expectiles; density forecasts; GDP growth; risks;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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