Discrete Choice Methods with Simulation | 
 enlarge | Author: Kenneth E. Train
Publisher: Cambridge University Press
Category: Book
List Price: $34.99
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Rating:  4 reviews
Sales Rank: 360763
Media: Paperback
Number Of Items: 1
Pages: 342
Shipping Weight (lbs): 1.1
Dimensions (in): 8.9 x 6 x 0.8
ISBN: 0521017157
Dewey Decimal Number: 003.56
EAN: 9780521017152
Publication Date: January 13, 2003
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Product Description
Focusing on the many advances that are made possible by simulation, this book describes the new generation of discrete choice methods. 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. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
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. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Download 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 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.
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| Customer Reviews:
excellent discussion of what the models mean March 17, 2005
10 out of 10 found this review helpful
If I could give this book six stars I would. It's simply one of the best statistics books I've ever read.
This book is very well-written by one of the experts in the field. It covers logit models and the various generalizations (GEV, mixed logit, probit, etc.) in detail, along with a thorough discussion of modern estimation of these models. What I find most useful about it is that the words-to-equations density is highly favorable. The equations you need are there, but the words you need are there too, making sure you understand the model assumptions inside and out. Each equation is explained thoroughly and the surrounding discussion probes the model to bring the reader to a critical understanding of what exactly is implied by the model. Too often complex statistical models are treated in a "black box" fashion. The dirty little secret is that it's easier for the author to do this. Train doesn't take the easy way out. The fact that his web site has truly excellent support--including a large number of webinars in addition to the more usual papers, software, etc.--makes this book a doubly valuable item. See http://elsa.berkeley.edu/~train/distant.html for even more.
Probably the best of its kind. Good for MS students and up April 24, 2005
9 out of 10 found this review helpful
-Enjoyable read
-Does not assume PhD level of econometrics
-Explanations are clear and concise
Actually, an advanced undergrad may find this book usefull as well.
Is this the best discrete choice methods book ever published?
Let y = 1 or 0 where 1 = yes, this is the best and 0 = No, the book is not the best. Also let P = Prob (y=1). My results show P = 0.98 (see forthcoming Econometrica article).
While finishing a Master's Thesis in applied econ which focuses on a multinomial logit model, I have sought good info on this topic. Not having gone through the rigors of a PhD program, I have gone through many of the important books and articles which address discrete choice modeling methods some of which address a post-doc audience only. Dr. Train's is by far the best I have encountered. His explanations are concise yet not too dense (see Amemiya). I first encountered many of the concepts in other publications but did understand them until reading Train's book. In my opinion Train has that rare quality of being, not only an exceptional economist, but quite enjoyable to read.
An excellent and thorough book March 8, 2006
5 out of 5 found this review helpful
This book is one of the best for introduction to Discrete choice models. I had been using Ben-Akiva & Lerman, but feel this book should be read along with that one for a better understanding of choice models. Also, Train covers recent advances in the field and provides a good introduction to Halton draws. He really makes sure you get the concepts and the online lecture series are really excellent.
Happy Customer April 1, 2008
Enough math to solidify the explanation and enough text to make it readable. Very well done.
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