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Discrete Choice Methods with Simulation
TL;DR: In this article, the authors describe the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling.
Abstract: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
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TL;DR: In this article, a detailed discussion of the unobserved heterogeneity in highway accident data and analysis is presented along with their strengths and weaknesses, as well as a summary of the fundamental issues and directions for future methodological work that address this problem.
843Â citations
TL;DR: This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.
818Â citations
TL;DR: In this article, the authors assess the effect of review ratings on usefulness and enjoyment and find that people perceive extreme ratings (positive or negative) as more useful and enjoyable than moderate ratings, giving rise to a U-shaped line with asymmetric effects.
438Â citations
TL;DR: An introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users, which incorporates numerous post-estimation tools.
Abstract: The community of choice modellers has expanded substantially over recent years, covering many disciplines and encompassing users with very different levels of econometric and computational skills. This paper presents an introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users. Apollo also incorporates numerous post-estimation tools, allows for both classical and Bayesian estimation, and permits advanced users to develop their own routines for new model structures.
316Â citations
TL;DR: In this article, the authors extend the literature by focusing on the panel mixed logit (ML) model with random parameters, which can take the dependency between choice situations into account, while in a stated choice survey usually multiple choice situations are presented to a single respondent.
Abstract: In each stated choice (SC) survey, there is an underlying experimental design from which the hypothetical choice situations are determined. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The main focus has been on the multinomial logit model, however this model is unable to take the dependency between choice situations into account, while in a stated choice survey usually multiple choice situations are presented to a single respondent. In this paper, we extend the literature by focusing on the panel mixed logit (ML) model with random parameters, which can take the above mentioned dependency into account. In deriving the analytical asymptotic variance-covariance matrix for the panel ML model, used to determine the efficiency of a design, we show that it is far more complex than the cross-sectional ML model (assuming independent choice observations). Case studies illustrate that it matters for which model the design is optimized, and that it seems that a panel ML model SC experiment needs less respondents than a cross-sectional ML experiment for the same level of reliability of the parameter estimates.
306Â citations