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Markov Switching Panel with Network Interaction Effects

Agudze, Komla Mawulom; Billio, Monica; Casarin, Roberto; Ravazzolo, Francesco
Working paper
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WP_CAMP_1_2018.pdf (1.920Mb)
URI
http://hdl.handle.net/11250/2477643
Date
2018-01
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  • Centre for Applied Macro- and Petroleum economics (CAMP) [104]
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.
Publisher
BI Norwegian Business School, Centre for Applied Macro- and Petroleum Economics
Series
CAMP Working Paper Series;1

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