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Book part
Publication date: 1 December 2016

Raffaella Calabrese and Johan A. Elkink

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…

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.

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Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

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Book part
Publication date: 14 September 2007

Frank S. Koppelman

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Handbook of Transport Modelling
Type: Book
ISBN: 978-0-08-045376-7

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Handbook of Transport Modelling
Type: Book
ISBN: 978-0-08-045376-7

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Book part
Publication date: 14 September 2007

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Handbook of Transport Modelling
Type: Book
ISBN: 978-0-08-045376-7

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Book part
Publication date: 1 January 2014

Eric J. Miller

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Handbook of Microsimulation Modelling
Type: Book
ISBN: 978-1-78350-570-8

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Book part
Publication date: 15 January 2010

Chandra R. Bhat and Naveen Eluru

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and…

Abstract

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and parsimonious multiple discrete-continuous extreme value (MDCEV) econometric approach to handle such multiple discreteness was formulated by Bhat (2005) within the broader Kuhn–Tucker (KT) multiple discrete-continuous economic consumer demand model of Wales and Woodland (1983). In this chapter, the focus is on presenting the basic MDCEV model structure, discussing its estimation and use in prediction, formulating extensions of the basic MDCEV structure, and presenting applications of the model. The paper examines several issues associated with the MDCEV model and other extant KT multiple discrete-continuous models. Specifically, the paper discusses the utility function form that enables clarity in the role of each parameter in the utility specification, presents identification considerations associated with both the utility functional form as well as the stochastic nature of the utility specification, extends the MDCEV model to the case of price variation across goods and to general error covariance structures, discusses the relationship between earlier KT-based multiple discrete-continuous models, and illustrates the many technical nuances and identification considerations of the multiple discrete-continuous model structure. Finally, we discuss the many applications of MDCEV model and its extensions in various fields.

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Choice Modelling: The State-of-the-art and The State-of-practice
Type: Book
ISBN: 978-1-84950-773-8

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Book part
Publication date: 19 November 2009

Michel Bierlaire and Thomas Robin

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Pedestrian Behavior
Type: Book
ISBN: 978-1-848-55750-5

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Book part
Publication date: 1 January 2014

Rolf Aaberge and Ugo Colombino

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Handbook of Microsimulation Modelling
Type: Book
ISBN: 978-1-78350-570-8

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