Abstract
AbstractBinary 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.
Highlights
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
We build on a pseudo maximum likelihood estimator (PMLE) for binary spatially autoregressive models proposed by Smirnov (2010), and extend it to cases of temporal and spatio-temporal interdependence
We examined a setup with just temporal autocorrelation, comparing the recursive importance sampling (RIS) and PMLE approach
Summary
Published 24 March 2021 erable methodological challenges in the presence of spatial and/or temporal autocorrelation resulting from interdependent outcomes across and within units. Simulation-based estimation strategies including Gibbs sampling (LeSage 2000) and Corresponding author Aya Kachi recursive importance sampling (RIS) (Beron and Vijverberg 2004) have been proposed to overcome this challenge. While these techniques promise to provide reliable estimates of spatial, Edited by Jeff Gill and more recently spatio-temporal interdependence (Franzese, Hays, and Cook 2016), they are computationally burdensome (see Calabrese and Elkink 2014).. While these techniques promise to provide reliable estimates of spatial, Edited by Jeff Gill and more recently spatio-temporal interdependence (Franzese, Hays, and Cook 2016), they are computationally burdensome (see Calabrese and Elkink 2014). As social scientists implement
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