Estimating binary spatial autoregressive models for rare events

Raffaella Calabrese, Johan Elkink

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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 simulationsand evaluate the prediction accuracy in empirical data on state failure.
Original languageEnglish
Title of host publicationAdvances in Econometrics
PublisherEmerald Publishing
Pages145-166
Volume37
ISBN (Electronic)9781785609855
ISBN (Print)9781785609862
DOIs
Publication statusPublished - 2016

Publication series

NameAdvances in Econometrics
Volume37
ISSN (Print)0731-9053

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