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BVARs: A Survey of the Recent Literature with an Application to the European Monetary System

Author

Listed:
  • Matteo Ciccarelli

    (Universidad de Alicante)

  • Alessandro Rebucci

    (International Monetary FundWashington D.C.)

Abstract

This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the general principle of Bayesian estimation applied to a VAR, we first present the methodology originally developed by Litterman (1986) and Doan et Al. (1984) and review alternative prior distributions. We then present several extensions of the basic model and discuss some issues in forecasting and structural analysis. An application to the estimation of a system of time-varying reaction functions for four European central banks under the European Monetary System (EMS) illustrates how results previously presented may be applied in practice.

Suggested Citation

  • Matteo Ciccarelli & Alessandro Rebucci, 2003. "BVARs: A Survey of the Recent Literature with an Application to the European Monetary System," Rivista di Politica Economica, SIPI Spa, vol. 93(5), pages 47-112, September.
    • Handle: RePEc:rpo:ripoec:v:93:y:2003:i:5:p:47-112
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    Citations

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    Cited by:

    1. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
    2. Stefano Grassi & Francesco Ravazzolo & Joaquin Vespignani & Giorgio Vocalelli, 2023. "Global Money Supply and Energy and Non-Energy Commodity Prices: A MS-TV-VAR Approach," CAMA Working Papers 2023-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Valeriu Nalban, 2015. "Do Bayesian Vector Autoregressive models improve density forecasting accuracy? The case of the Czech Republic and Romania," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(1), pages 60-74, March.
    4. Ahmed, Abdullahi D. & Huo, Rui, 2019. "Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence, information transmission and market co-movement," Economic Modelling, Elsevier, vol. 79(C), pages 28-46.
    5. Altavilla, Carlo & Ciccarelli, Matteo, 2010. "Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts," Economic Modelling, Elsevier, vol. 27(1), pages 237-253, January.
    6. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    7. Volkan Hacioglu, 2015. "Bayesian Expectations and Strategic Complementarity: Implications for Macroeconomic Stability," Post-Print hal-01404402, HAL.
    8. Ahmed, Abdullahi D. & Huo, Rui, 2018. "China–Africa financial markets linkages: Volatility and interdependence," Journal of Policy Modeling, Elsevier, vol. 40(6), pages 1140-1164.
    9. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    10. Tsagkanos, Athanasios & Argyropoulou, Despoina & Androulakis, Georgios, 2022. "Asymmetric economic effects via the dependence structure of green bonds and financial stress index," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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