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.

Speaker Biography： 

Francesco Ravazzolo is Full Professor of Econometrics at Faculty of Economics and Management at Free University of Bozen/Bolzano and visiting Professor at Center for Applied Macro and commodity Prices, BI Norwegian Business School. 
His research focuses on Bayesian econometrics, energy economics, financial econometrics and macroeconometrics. He has published in several leading academic journals. 
Francesco serves the academia in several roles: he is in the editorial board of the following journals: Annals of Applied Statistics; International Journal of Forecasting; Journal of Applied Econometrics; Studies in Nonlinear Dynamics and Econometrics. He is also member of the executive committee of Society of Nonlinear Dynamics and Econometrics. His activities have been reviewed in several newspapers and magazines, such as Wall Street Journal, The Telegraph, Corriere della Sera, Academia.