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A Panel Data Model with Generalized Higher-Order Network Effects

In: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology

Author

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  • Badi H. Baltagi
  • Sophia Ding
  • Peter H. Egger

Abstract

Many data situations require the consideration of network effects among the cross-sectional units of observation. In this chapter, the authors present a generalized panel model which accounts for two features: (i) network effects present through weighted dependent variables as regressors, exogenous variables, as well as the error components, and (ii) higher-order network effects due to ex ante unknown network decay functions or the presence of multiplex (multi-layer) networks among all of those. The authors outline the model, the basic assumptions, and present simulation results.

Suggested Citation

  • 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.
    • Handle: RePEc:eme:aecozz:s0731-90532021000043b002
    DOI: 10.1108/S0731-90532021000043B002
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    References listed on IDEAS

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    1. Cem Ertur & Wilfried Koch, 2007. "Growth, technological interdependence and spatial externalities: theory and evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1033-1062.
    2. 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.
    3. Wansbeek, Tom & Kapteyn, Arie, 1982. "A Class of Decompositions of the Variance-Covariance Matrix of a Generalized Error Components Model," Econometrica, Econometric Society, vol. 50(3), pages 713-724, May.
    4. Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May.
    5. 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.
    6. 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.
    7. M. Hashem Pesaran & Badi H. Baltagi, 2007. "Heterogeneity and cross section dependence in panel data models: theory and applications introduction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 229-232.
    8. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    9. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
    10. Jushan Bai & Badi Baltagi & Hashem Pesaran, 2016. "Cross‐Sectional Dependence in Panel Data Models: A Special Issue," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 1-3, January.
    11. Egger, Peter & Pfaffermayr, Michael & Winner, Hannes, 2005. "An unbalanced spatial panel data approach to US state tax competition," Economics Letters, Elsevier, vol. 88(3), pages 329-335, September.
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    Cited by:

    1. 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," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.

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    More about this item

    Keywords

    Spatial and network interdependence; panel data; higher-order network effects; random effects; error components; maximum-likelihood estimation; C23; C33; C34;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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