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Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model

Author

Listed:
  • Monica Billio

    (Department of Economics, University of Venice C� Foscari)

  • Roberto Casarin

    (Department of Economics, University of Venice C� Foscari)

  • Francesco Ravazzolo

    (Norges Bank and BI Norwegian Business School)

  • Herman K. van Dijk

    (Econometric Institute, Erasmus University Rotterdam and Econometrics Department VU University Amsterdam)

Abstract

Interactions between eurozone and United States booms and busts and among major eurozone economies are analyzed by introducing a panel Markov-switching VAR model. The model is well suitable for a multi-country cyclical analysis and accommodates changes in low and high data frequencies and endogenous time-varying transition matrices of the country-specific Markov chains. The transition matrix of each Markov chain depends on its own past history and on the history of other chains, thus allowing for modelling the interactions between cycles. An endogenous common eurozone cycle is derived by aggregating country-specific cycles. The model is estimated using a simulation based Bayesian approach in which an efficient multi-move algorithm is defined to draw time-varying Markov-switching chains. Using real and financial data on industrial production growth and credit spread for all countries, our main empirical results are as follows. Recession, slow recovery and expansion are empirically identified as three regimes with slow recovery becoming persistent in the eurozone in recent years differing from the US. US and eurozone cycles are not fully synchronized over the 1991- 2013 period, with evidence of more recessions in the eurozone, in particular during the 90's. Larger synchronization across regions occurs at beginning of the financial crisis but recently more heterogeneity takes place. Cluster analysis yields a group of core countries: Germany, France and Netherlands and a group of peripheral countries Spain and Italy. Reinforcement effects in the recession probabilities and in the probabilities of exiting recessions occur for both eurozone and US with substantial differences in phase transitions within the eurozone. Finally, credit spreads provide accurate predictive content for business cycle fluctuations. A credit shock results in statistically significant negative industrial production growth for several months in Germany, Spain and US. Our empirical result may serve as important information for the specification of a coordinated policy between the eurozone and the US and within the eurozone.

Suggested Citation

  • Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model," Working Papers 2013:17, Department of Economics, University of Venice "Ca' Foscari", revised 2014.
  • Handle: RePEc:ven:wpaper:2013:17
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    3. Netšunajev, Aleksei & Glass, Katharina, 2017. "Uncertainty and employment dynamics in the euro area and the US," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 48-62.
    4. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
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    6. Kaufmann, Sylvia, 2015. "K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?," Journal of Econometrics, Elsevier, vol. 187(1), pages 82-94.
    7. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
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    More about this item

    Keywords

    Bayesian Model; Panel VAR; Markov-switching; International Business Cycles; Interaction mechanisms.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • 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
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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