Markov switching panel with endogenous synchronization effects
Markov switching panel with endogenous synchronization effects
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DOI：
10.1016/j.jeconom.2021.04.004
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发表时间：
2022-08-10
影响因子：
6.3
通讯作者：
Ravazzolo, Francesco
中科院分区：
文献类型：
--
作者：
Agudze, Komla M.;Billio, Monica;Ravazzolo, Francesco
本文介绍了一种具有多层网络效应的新型动态面板模型。系列特定的潜在马尔可夫链过程驱动可观察过程的动态，并且隐藏链之间的几种类型的相互作用效应允许潜在过程和可观察过程的不同程度的内生同步。这种交互是由具有外源和内源连接层的多层网络驱动的。我们提供了模型的一些理论属性，开发了贝叶斯推理框架和高效的马尔可夫链蒙特卡罗算法，用于估计参数、潜在状态和内生网络层。对美国各州同步指标的应用表明，美国经济的同步性是由各州之间的网络效应产生的。多层网络的纳入提供了一种新工具，用于衡量影响美国各州之间连通性的公共政策的效果，例如流动性限制或就业支持计划。所提出的新模型和相关推论是通用的，并且可以在广泛的数据集中找到应用，其中内生相互作用效应的提取是相关且令人感兴趣的。 (C) 2021 作者。由 Elsevier B.V. 出版
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. (C) 2021 The Authors. Published by Elsevier B.V.