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A Panel Data Model with Generalized Higher-Order Network Effects

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Abstract

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

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  • Badi H. Baltagi & Sophia Ding & Peter H. Egger, 2020. "A Panel Data Model with Generalized Higher-Order Network Effects," Center for Policy Research Working Papers 233, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:233
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    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;
    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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