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Bayesian Econometrics

Author

Listed:
  • Mauro Bernardi

    (Department of Statistics, University of Padova, 39100 Padova, Italy)

  • Stefano Grassi

    (Department of Economics and Finance, University of Rome ‘Tor Vergata’, 00133 Rome, Italy)

  • Francesco Ravazzolo

    (Faculty of Economics and Management, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
    Centre for Applied Macroeconomics and Commodity Prices, BI Norwegian Business School, 0442 Oslo, Norway
    Rimini Centre for Economic Analysis (RCEA), Waterloo, ON N2L3C5, Canada)

Abstract

The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics.

Suggested Citation

  • Mauro Bernardi & Stefano Grassi & Francesco Ravazzolo, 2020. "Bayesian Econometrics," JRFM, MDPI, vol. 13(11), pages 1-2, October.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:11:p:257-:d:436904
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    References listed on IDEAS

    as
    1. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," JRFM, MDPI, vol. 12(3), pages 1-18, September.
    2. Chiara Limongi Concetto & Francesco Ravazzolo, 2019. "Optimism in Financial Markets: Stock Market Returns and Investor Sentiments," JRFM, MDPI, vol. 12(2), pages 1-14, May.
    3. Marco Lorusso & Luca Pieroni, 2019. "Disentangling Civilian and Military Spending Shocks: A Bayesian DSGE Approach for the US Economy," JRFM, MDPI, vol. 12(3), pages 1-41, September.
    4. Nguyen Ngoc Thach, 2020. "How to Explain When the ES Is Lower Than One? A Bayesian Nonlinear Mixed-Effects Approach," JRFM, MDPI, vol. 13(2), pages 1-17, February.
    Full references (including those not matched with items on IDEAS)

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