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SIMULTANEOUS EQUATIONS MODELS WITH HIGHER-ORDER SPATIAL OR SOCIAL NETWORK INTERACTIONS

Published online by Cambridge University Press:  28 March 2022

David M. Drukker ,
Peter H. Egger  and
Ingmar R. Prucha
David M. Drukker
Affiliation:
Department of Economics and International Business, Sam Houston State University
Peter H. Egger
Affiliation:
ETH Zurich
Ingmar R. Prucha*
Affiliation:
Department of Economics, University of Maryland
*
Address correspondence to Ingmar R. Prucha, Department of Economics, University of Maryland, College Park, MD 20742, USA; e-mail: prucha@umd.edu.
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Abstract

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This paper develops an estimation methodology for network data generated from a system of simultaneous equations, which allows for network interdependencies via spatial lags in the endogenous and exogenous variables, as well as in the disturbances. By allowing for higher-order spatial lags, our specification provides important flexibility in modeling network interactions. The estimation methodology builds, among others, on the two-step generalized method of moments estimation approach introduced in Kelejian and Prucha (1998, Journal of Real Estate Finance and Economics 17, 99–121; 1999, International Economic Review 40, 509–533; 2004, Journal of Econometrics 118, 27–50). The paper considers limited and full information estimators, and one- and two-step estimators, and establishes their asymptotic properties. In contrast to some of the earlier two-step estimation literature, our asymptotic results facilitate joint tests for the absence of all forms of network spillovers.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 6: SPECIAL ISSUE IN HONOR OF BENEDIKT M PÖTSCHER , December 2023 , pp. 1154 - 1201
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

We are grateful to the editor, a guest editor, and three anonymous referees for their very helpful comments. We gratefully acknowledge financial support from the National Institute of Health through SBIR grants R43 AG027622 and R44 AG027622. We also thank the CESifo for their hospitality.

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