Estimating Binary Spatial Autoregressive Models for Rare Events

Abstract

The most used spatial regression models for binary-dependent variable consider a symmetric link function, such as the logistic or the probit models. When the dependent variable represents a rare event, a symmetric link function can underestimate the probability that the rare event occurs. Following Calabrese and Osmetti (2013), we suggest the quantile function of the generalized extreme value (GEV) distribution as link function in a spatial generalized linear model and we call this model the spatial GEV (SGEV) regression model. To estimate the parameters of such model, a modified version of the Gibbs sampling method of Wang and Dey (2010) is proposed. We analyze the performance of our model by Monte Carlo simulations and evaluate the prediction accuracy in empirical data on state failure.

Citation

Calabrese, R. and Elkink, J.A. (2016), "Estimating Binary Spatial Autoregressive Models for Rare Events", Spatial Econometrics: Qualitative and Limited Dependent Variables (Advances in Econometrics, Vol. 37), Emerald Group Publishing Limited, Leeds, pp. 145-166. https://doi.org/10.1108/S0731-905320160000037012