Efficient Estimation of Nested Logit Models
Author : David M. Brownstone
Publisher :
Page : 34 pages
File Size : 40,38 MB
Release : 1985
Category : Logits
ISBN :
Author : David M. Brownstone
Publisher :
Page : 34 pages
File Size : 40,38 MB
Release : 1985
Category : Logits
ISBN :
Author : Kenneth A. SMALL
Publisher :
Page : pages
File Size : 21,88 MB
Release : 1982
Category :
ISBN :
Author : Kenneth A. Small
Publisher :
Page : 28 pages
File Size : 43,40 MB
Release : 1982
Category :
ISBN :
Author : Stephen Rhys Cosslett
Publisher :
Page : 498 pages
File Size : 40,44 MB
Release : 1979
Category : Choice of transportation
ISBN :
Author : Kenneth Train
Publisher : Cambridge University Press
Page : 399 pages
File Size : 16,47 MB
Release : 2009-07-06
Category : Business & Economics
ISBN : 0521766559
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 : Alfred DeMaris
Publisher : SAGE
Page : 100 pages
File Size : 21,64 MB
Release : 1992-06-06
Category : Business & Economics
ISBN : 9780803943773
Logit models : theoretical background. Logit models for multidimensional tables. Logistic regression. Advanced topics in logistic regression. Appendix : Computer routines.
Author : David A. Hensher
Publisher :
Page : 424 pages
File Size : 34,91 MB
Release : 1978
Category : Business & Economics
ISBN :
Author : Laurie Anne Garrow
Publisher :
Page : pages
File Size : 47,2 MB
Release : 2004
Category :
ISBN :
A second theoretical contribution of this work relates to efficient estimation of nested logit models for choice-based samples. Currently, the benefit of collecting choice-based samples diminishes when modeling consumers' behavior using NL models because of the need to use consistent estimators that are often inefficient and/or complicated to implement. In contrast, benefits of using choice-based samples are retained when using the simple multinomial logit model because, under conditions that are relatively easy to satisfy in practice, the exogenous sample maximum likelihood estimator can be used. This study shows the exogenous sample maximum likelihood estimator can also be used with choice-based samples for NL models.
Author : C. Vasudev
Publisher : New Age International
Page : 25 pages
File Size : 45,70 MB
Release : 2006
Category : Graph theory
ISBN : 812241737X
Over 1500 problems are used to illustrate concepts, related to different topics, and introduce applications.Over 1000 exercises in the text with many different types of questions posed. Precise mathematical language is used without excessive formalism and abstraction. Care has been taken to balance the mix of notation and words in mathematical statements. Problem sets are stated clearly and unambiguously, and all are carefully graded for various levels of difficulty. This text has been carefully designed for flexible use.
Author : John H. Aldrich
Publisher : SAGE
Page : 100 pages
File Size : 41,58 MB
Release : 1984-11
Category : Mathematics
ISBN : 9780803921337
After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.