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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

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  • Badi H. Baltagi
  • Peter H. Egger
  • Michaela Kesina

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.

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," CESifo Working Paper Series 9579, CESifo.
  • Handle: RePEc:ces:ceswps:_9579
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2016. "Bayesian Spatial Bivariate Panel Probit Estimation," Advances in Econometrics, in: Spatial Econometrics: Qualitative and Limited Dependent Variables, volume 37, pages 119-144, Emerald Group Publishing Limited.
    3. Devereux, Michael P. & Lockwood, Ben & Redoano, Michela, 2008. "Do countries compete over corporate tax rates?," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 1210-1235, June.
    4. Badi H. Baltagi, 2015. "Seemingly Unrelated Regressions," Springer Texts in Business and Economics, in: Solutions Manual for Econometrics, edition 3, chapter 0, pages 233-257, Springer.
    5. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    6. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    7. James R. Markusen, 2004. "Multinational Firms and the Theory of International Trade," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633078, December.
    8. Marc J. Melitz, 2003. "The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity," Econometrica, Econometric Society, vol. 71(6), pages 1695-1725, November.
    9. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    10. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    11. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    12. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2017. "Determinants of firm-level domestic sales and exports with spillovers: Evidence from China," Journal of Econometrics, Elsevier, vol. 199(2), pages 184-201.
    13. Bruce A. Blonigen & Lionel Fontagné & Nicholas Sly & Farid Toubal, 2019. "Cherries for Sale: The Incidence and Timing of Cross-border M&A," World Scientific Book Chapters, in: Foreign Direct Investment, chapter 4, pages 121-177, World Scientific Publishing Co. Pte. Ltd..
    14. Harald Badinger & Peter Egger, 2015. "Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(1), pages 11-35, March.
    15. Ethan Cohen‐Cole & Xiaodong Liu & Yves Zenou, 2018. "Multivariate choices and identification of social interactions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 165-178, March.
    16. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    17. T. Arduini, 2016. "Distribution Free Estimation of Spatial Autoregressive Binary Choice Panel Data Models," Working Papers wp1052, Dipartimento Scienze Economiche, Universita' di Bologna.
    18. Baltagi, Badi H. & Bresson, Georges, 2011. "Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris," Journal of Urban Economics, Elsevier, vol. 69(1), pages 24-42, January.
    19. Elhanan Helpman & Marc J. Melitz & Stephen R. Yeaple, 2004. "Export Versus FDI with Heterogeneous Firms," American Economic Review, American Economic Association, vol. 94(1), pages 300-316, March.
    20. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2019. "Contagious exporting and foreign ownership: Evidence from firms in Shanghai using a Bayesian spatial bivariate probit model," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 125-146.
    21. Joris Pinkse & Margaret Slade & Lihong Shen, 2006. "Dynamic Spatial Discrete Choice Using One-step GMM: An Application to Mine Operating Decisions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 53-99.
    22. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    23. Bruce A. Blonigen & Lionel Fontagné & Nicholas Sly & Farid Toubal, 2019. "Cherries for Sale: The Incidence and Timing of Cross-border M&A," World Scientific Book Chapters, in: Foreign Direct Investment, chapter 4, pages 121-177, World Scientific Publishing Co. Pte. Ltd..
    24. Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    25. Xiaokun Wang & Kara M. Kockelman, 2009. "Baysian Inference For Ordered Response Data With A Dynamic Spatial‐Ordered Probit Model," Journal of Regional Science, Wiley Blackwell, vol. 49(5), pages 877-913, December.
    26. Badi H. Baltagi & Sophia Ding & Peter H. Egger, 2022. "A Panel Data Model with Generalized Higher-Order Network Effects," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 9-35, Emerald Group Publishing Limited.
    27. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    28. Xiaodong Liu, 2014. "Identification and Efficient Estimation of Simultaneous Equations Network Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 516-536, October.
    29. Harald Badinger & Peter Egger, 2013. "Estimation and testing of higher-order spatial autoregressive panel data error component models," Journal of Geographical Systems, Springer, vol. 15(4), pages 453-489, October.
    30. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    31. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    32. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Discussion Paper 2013-061, Tilburg University, Center for Economic Research.
    33. Badi H. Baltagi & Ying Deng, 2015. "EC3SLS Estimator for a Simultaneous System of Spatial Autoregressive Equations with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 659-694, December.
    34. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.
    35. Andrew Bernard & Joachim Wagner, 2001. "Export entry and exit by German firms," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 137(1), pages 105-123, March.
    36. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    37. Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
    38. Liu, Xiaodong & Lee, Lung-fei & Bollinger, Christopher R., 2010. "An efficient GMM estimator of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 159(2), pages 303-319, December.
    39. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    40. Carrion-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2009. "Land Use Change: A Spatial Multinomial Choice Analysis," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49403, Agricultural and Applied Economics Association.
    41. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    42. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Other publications TiSEM d63bf400-7ff2-4a1c-8067-1, Tilburg University, School of Economics and Management.
    43. Kurt J. Beron & Wim P. M. Vijverberg, 2004. "Probit in a Spatial Context: A Monte Carlo Analysis," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 8, pages 169-195, Springer.
    44. Lee, Lung-fei & Liu, Xiaodong, 2010. "Efficient Gmm Estimation Of High Order Spatial Autoregressive Models With Autoregressive Disturbances," Econometric Theory, Cambridge University Press, vol. 26(1), pages 187-230, February.
    45. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
    46. Yang, Kai & Lee, Lung-fei, 2017. "Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 196(1), pages 196-214.
    47. Lin, Xu & Lee, Lung-fei, 2010. "GMM estimation of spatial autoregressive models with unknown heteroskedasticity," Journal of Econometrics, Elsevier, vol. 157(1), pages 34-52, July.
    48. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
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    More about this item

    Keywords

    network models; spatial models; higher-order network interdependence; multivariate panel probit; Bayesian estimation; firm-level data; Chinese firms;
    All these 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

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