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Bayesian Estimation of Multivariate Panel Probits with Higher-Order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong

Badi Baltagi (), Peter Egger () and Michaela Kesina

No 9579, CESifo Working Paper Series from CESifo

Abstract: This paper proposes a Bayesian estimation framework for panel-data sets with binary dependent variables where a large number of cross-sectional units is observed over a short period of time, and cross-sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modelling the decay function in network neighbourliness (e.g., by disentangling the importance of rings of neighbors) or towards allowing for several channels of interdependence whose relative importance is unknown ex ante. Besides the flexible parameterization of cross-sectional dependence, the approach allows for simultaneity of the equations. These features should make the approach interesting for applications in a host of contexts involving structural and reduced-form models of multivariate choice problems at micro-, meso-, and macroeconomic levels. The paper outlines the estimation approach, illustrates its suitability by simulation examples, and provides an application to study exporting and foreign ownership among potentially interdependent firms in the specialized and transport machinery sector in the province of Guangdong.

Keywords: network models; spatial models; higher-order network interdependence; multivariate panel probit; Bayesian estimation; firm-level data; Chinese firms (search for similar items in EconPapers)
JEL-codes: C11 C31 C35 F14 F23 L22 R10 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-cwa and nep-net
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