IDEAS home Printed from https://ideas.repec.org/p/max/cprwps/247.html

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

Author

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 neighborliness (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.

Suggested Citation

  • Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian Estimation of Multivariate Panel Probits with Higher-order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong," Center for Policy Research Working Papers 247, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:247
    as

    Download full text from publisher

    File URL: https://surface.syr.edu/cpr/381/
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fei Jin & Lung‐fei Lee & Kai Yang, 2024. "Best linear and quadratic moments for spatial econometric models with an application to spatial interdependence patterns of employment growth in US counties," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 640-658, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:max:cprwps:247. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katrina Fiacchi (email available below). General contact details of provider: https://edirc.repec.org/data/cpsyrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.