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

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.

Automated summary

Binary outcome models are commonly used in social sciences and economics, but can be difficult to estimate with interdependent data structures. To address this issue, analytically-tractable pseudo maximum likelihood estimators and an implementation strategy to increase computational efficiency have been introduced, significantly reducing the computational burden. Monte Carlo experiments demonstrate that these estimators can recover parameter values with comparable accuracy to commonly used methods, but at a fraction of the computational cost.