Markov Switching Panel with Network Interaction Effects
Komla Mawulom Agudze,
Monica Billio (),
Roberto Casarin () and
No No 1/2018, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School
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.
Keywords: Bayesian inference; interacting Markov chains; Metropolis adjusted Langevin; panel Markov-switching. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
https://brage.bibsys.no/xmlui/bitstream/handle/112 ... quence=1&isAllowed=y
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0059
Access Statistics for this paper
More papers in Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School Contact information at EDIRC.
Bibliographic data for series maintained by Helene Olsen ().