Discrete Choice Methods with Simulation
TL;DR: Discrete Choice Methods with Simulation by Kenneth Train has been available in the second edition since 2009 and contains two additional chapters, one on endogenous regressors and one on the expectation–maximization (EM) algorithm.
Abstract: Discrete Choice Methods with Simulation by Kenneth Train has been available in the second edition since 2009. The book is published by Cambridge University Press and is also available for download ...
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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.
843 citations
Cites background or methods from "Discrete Choice Methods with Simula..."
...The resulting random parameters multinomial logit injury-severity probabilities are (see Bhat, 1998, McFadden and Train, 2000; Train, 2009), k mi m eP k f | d e ik im β x β x β β , (7) where iP k is the probability of injury severity k....
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...The estimation of random parameters models is typically achieved with maximum simulated likelihood (for more on this technique, see Bhat, 2001, 2003; Train, 2009)....
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...11 parameters multinomial logit injury-severity probabilities are (see Bhat, 1998, McFadden and Train, 2000; Train, 2009),...
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...The estimation of random parameters models is typically achieved with maximum simulated likelihood (for more on this technique, see Bhat, 2001, 2003; Train, 2009)....
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TL;DR: In this paper, the authors present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.
678 citations
TL;DR: In this article, rational inattention was used to model the decision maker's optimal strategy for discrete alternatives with imperfect information about their values, which results in choosing probabilistically in line with a modied multinomial logit model.
Abstract: Individuals must often choose among discrete alternatives with imperfect information about their values. Before choosing, they may have an opportunity to study the options, but doing so is costly. This costly information acquisition creates new choices such as the number of and types of questions to ask. We model these situations using the rational inattention approach to information frictions. We nd that the decision maker’s optimal strategy results in choosing probabilistically in line with a modied multinomial logit model. The modication arises because the decision maker’s prior knowledge and attention allocation strategy aect his evaluation of the alternatives. When the options are a priori homogeneous, the standard logit model emerges.
578 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.
438 citations
Cites background from "Discrete Choice Methods with Simula..."
...Also, Train (2011) goes even further and stresses the fact that deterministic measures should not be used in these types of models; rather, probabilistic indicators should be employed....
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TL;DR: In this article, the authors present a comprehensive review of studies on consumer preferences for electric vehicles, aiming to better inform policy-makers and give direction to further research, and discuss a research agenda to improve EV consumer preference studies and give recommendations for further research.
407 citations
Cites methods from "Discrete Choice Methods with Simula..."
...Thus, some studies used nested logit models to relax the restriction of IIA (Train, 2003)....
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...However, MNL assumes independence from irrelevant alternatives (IIAs), which does not hold in most cases....
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References
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Book•
01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
28,298 citations
Book•
01 Jan 2003TL;DR: In this paper, the authors describe the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation, and compare simulation-assisted estimation procedures, including maximum simulated likelihood, method of simulated moments, and methods of simulated scores.
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. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
7,768 citations
Posted Content•
TL;DR: Simulation estimation methods for limited dependent variable (LDV) models that employ Monte Carlo simulation techniques to overcome computational problems in such models make it possible to estimate LDV models that are computationally intractable using classical estimation methods.
387 citations
"Discrete Choice Methods with Simula..." refers methods in this paper
...Chapter 10 combines classical theory for M-estimation with simulation and discusses simulation-based estimation much in the spirit of Hajivassiliou and Ruud (1994) and others....
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TL;DR: Monte Carlo experiments for the mixed logit model indicate the superior performance of the proposed Gaussian quadrature extension over simulation techniques.
360 citations
TL;DR: In this paper, the authors describe a simple, generic and highly accurate efficient importance sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals.
280 citations
"Discrete Choice Methods with Simula..." refers background in this paper
...Since importance sampling is introduced, the concept of efficient importance sampling (Richard and Zhang, 2007) would fit very well into the book....
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