Markov switching panel with endogenous synchronization effects

Komla M. Agudze, Monica Billio, Roberto Casarin and Francesco Ravazzolo

Journal of Econometrics, 2022, vol. 230, issue 2, 281-298

Abstract: This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may find application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest.

Keywords: Bayesian inference; Interacting Markov chains; Multi-layer networks; Panel Markov-switching (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C23 C55 (search for similar items in EconPapers)
Date: 2022
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Working Paper: Markov Switching Panel with Endogenous Synchronization Effects (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:230:y:2022:i:2:p:281-298

DOI: 10.1016/j.jeconom.2021.04.004

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