Author: Dinesh Ambat Gopinath
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description

Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399

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Book Description
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 stimulated 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. The 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.

Author: Dinesh Ambat Gopinath
Publisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 366

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Book Description

Author: Moshe Ben-Akiva
Publisher: MIT Press
ISBN: 0262536404
Category : Architecture
Languages : en
Pages : 414

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Book Description
Discrete Choice Analysis presents these results in such a way that they are fully accessible to the range of students and professionals who are involved in modelling demand and consumer behavior in general or specifically in transportation - whether from the point of view of the design of transit systems, urban and transport economics, public policy, operations research, or systems management and planning. The methods of discrete choice analysis and their applications in the modelling of transportation systems constitute a comparatively new field that has largely evolved over the past 15 years. Since its inception, however, the field has developed rapidly, and this is the first text and reference work to cover the material systematically, bringing together the scattered and often inaccessible results for graduate students and professionals. Discrete Choice Analysis presents these results in such a way that they are fully accessible to the range of students and professionals who are involved in modelling demand and consumer behavior in general or specifically in transportation - whether from the point of view of the design of transit systems, urban and transport economics, public policy, operations research, or systems management and planning. The introductory chapter presents the background of discrete choice analysis and context of transportation demand forecasting. Subsequent chapters cover, among other topics, the theories of individual choice behavior, binary and multinomial choice models, aggregate forecasting techniques, estimation methods, tests used in the process of model development, sampling theory, the nested-logit model, and systems of models. Discrete Choice Analysis is ninth in the MIT Press Series in Transportation Studies, edited by Marvin Manheim.

Author: Jin Gyo Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
The finite normal mixture model has emerged as a dominant methodology for assessing heterogeneity in choice models. Although it extends the classic mixture models by allowing within component variablility, it requires that a relatively large number of models be separately estimated and fairly difficult test procedures to determine the "correct" number of mixing components. We present a very general formulation, based on Dirichlet Process Piror, which yields the number and composition of mixing components a posteriori, obviating the need for post hoc test procedures and is capable of approximating any target heterogeneity distribution. Adapting Stephens' (2000) algorithm allows the determination of "substantively" different clusters, as well as a way to sidestep problems arising from label-switching and overlapping mixtures. These methods are illustrated both on simulated data and A.C. Nielsen scanner panel data for liquid detergents. We find that the large number of mixing components required to adequately represent the heterogeneity distribution can be reduced in practice to a far smaller number of segments of managerial relevance.

Author: David A. Hensher
Publisher: Routledge
ISBN: 1351140744
Category : Business & Economics
Languages : en
Pages : 280

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Book Description
Originally published in 1981. Discrete-choice modelling is an area of econometrics where significant advances have been made at the research level. This book presents an overview of these advances, explaining the theory underlying the model, and explores its various applications. It shows how operational choice models can be used, and how they are particularly useful for a better understanding of consumer demand theory. It discusses particular problems connected with the model and its use, and reports on the authors’ own empirical research. This is a comprehensive survey of research developments in discrete choice modelling and its applications.

Author: Antonio Páez
Publisher: Springer Nature
ISBN: 303120719X
Category : Mathematics
Languages : en
Pages : 349

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Book Description
This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.

Author: Stephane Hess
Publisher: Emerald Group Publishing
ISBN: 1849507724
Category : Transportation
Languages : en
Pages : 639

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Book Description
Contains a selection of the best theoretical and applied papers from the inaugural International Choice Modelling Conference. The conference was organised by the Institute for Transport Studies at the University of Leeds and held in Harrogate, North Yorkshire on 30 March to 1 April 2009.

Author: Ali Yazdizadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 75

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Book Description
Discrete choice models and their strength to predict individual choices mostly depend on the quality of datasets that have been used for model generation. However, even the most comprehensive and detailed datasets are not able to observe all factors pertinent to someone’s choice. This issue in the choice modelling literature has been addressed as unobserved heterogeneity, which means that individuals across populations are not affected identically by alternative attributes. Furthermore, such variation in preferences across populations and their sources are not always recognized by researchers. There are different methods to capture unobserved heterogeneity proposed in the discrete choice literature among which the random parameters approach, also referred to as mixed logit models, the latent class approach and the agent effect approach are the most well know methods. The main contribution of this study is to extend the formulation of LC-MMNL model to capture the agent effect by including a random term in the utility function of the model. Three types of models, Mixed Multinomial Logit (MMNL), Latent Class Mixed Multinomial Logit (LC-MMNL) and Agent Effect Latent Class Mixed Multinomial Logit (AGLC-MMNL) have been generated and the results compared. Considering agent effect simultaneously with other sources of unobserved heterogeneity in a latent class context demonstrates improvement in terms of model fit as well as cross section validation. It enables us to generate a latent class model with a larger number of classes explaining more heterogeneity across the population of a neighborhood location choice study. The AGLC-MMNL model is able to detect four distinct classes of individuals in Montreal, exhibiting different behaviours while facing neighborhood location choices in the context of a Discrete Choice Experiment. The classes of the model are able to explain different behaviours of individuals based on their income level, whether they are transit or car oriented, and the importance of privacy to them.

Author: Simon P. Anderson
Publisher: MIT Press
ISBN: 9780262011280
Category : Business & Economics
Languages : en
Pages : 454

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Book Description
"The discrete choice approach provides an ideal framework for describing the demands for differentiated products and can be used for studying most product differentiation models in the literature. By introducing extra dimensions of product heterogeneity, the framework also provides richer models of firm location and product selection."--BOOK JACKET.