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Markov Switching Panel with Network Interaction Effects

Author

Listed:
  • Komla Mawulom Agudze
  • Monica Billio
  • Roberto Casarin
  • Francesco Ravazzolo

Abstract

The paper introduces a new dynamic panel model for large data sets of time series, each of them characterized by a series-specific Markov switching process. By introducing a neighbourhood system based on a network structure, the model accounts for local and global interactions among the switching processes. We develop an efficient Markov Chain Monte Carlo (MCMC) algorithm for the posterior approximation based on the Metropolis adjusted Langevin sampling method. We study efficiency and convergence of the proposed MCMC algorithm through several simulation experiments. In the empirical application, we deal with US states coincident indices, produced by the Federal Reserve Bank of Philadelphia, and find evidence that local interactions of state-level cycles with geographically and economically networks play a substantial role in the common movements of US regional business cycles.

Suggested Citation

  • Komla Mawulom Agudze & Monica Billio & Roberto Casarin & Francesco Ravazzolo, 2018. "Markov Switching Panel with Network Interaction Effects," Working Papers No 1/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0059
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    Citations

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    Cited by:

    1. Michael T. Owyang & Jeremy Piger & Daniel Soques, 2022. "Contagious switching," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 415-432, March.
    2. Antonio Pacifico, 2019. "Structural Panel Bayesian VAR Model to Deal with Model Misspecification and Unobserved Heterogeneity Problems," Econometrics, MDPI, vol. 7(1), pages 1-24, March.

    More about this item

    Keywords

    Bayesian inference; interacting Markov chains; Metropolis adjusted Langevin; panel Markov-switching.;
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