Markov Switching Panel with Network Interaction Effects
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.