A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

Wucherpfennig, Julian and Kachi, Aya and Bormann, Nils-Christian and Hunziker, Philipp. (2021) A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence. Political Analysis, 29 (3). pp. 1-7.

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Official URL: https://edoc.unibas.ch/84115/

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Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > International Political Economy and Energy Policy (Kachi)
UniBasel Contributors:Kachi, Aya
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Cambridge University Press
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:30 Jul 2021 13:28
Deposited On:30 Jul 2021 13:28

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