Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/252096 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 9579
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
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.
Subjects: 
network models
spatial models
higher-order network interdependence
multivariate panel probit
Bayesian estimation
firm-level data
Chinese firms
JEL: 
C11
C31
C35
F14
F23
L22
R10
Document Type: 
Working Paper
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