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The Sensitivity of Spatial Regression Models to Network Misspecification

  • Sebastian Juhl (a1)

Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by  $\boldsymbol{W}$ , prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of  $\boldsymbol{W}$ . It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of  $\boldsymbol{W}$ , uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

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Author’s note: A previous version of this project has been presented at the EPSA conference 2018 in Vienna (Austria). I thank Thomas Bräuninger, Roni Lehrer, Eric Neumayer, Katrin Paula, Thomas Plümper, Richard Traunmüller, Laron Williams, as well as the reviewers and the editor Jeff Gill for providing excellent comments. Supplementary materials are available on the Political Analysis website and replication materials can be found on the Political Analysis Dataverse (Juhl 2019). This research was supported by the German Research Foundation (DFG) via the SFB 884 on “The Political Economy of Reforms” (Project C2) and the University of Mannheim’s Graduate School of Economic and Social Sciences (GESS).

Contributing Editor: Jeff Gill

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Political Analysis
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