Discrete Choice Methods with Simulation by Kenneth E. Train

By Kenneth Train. 2003

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Citations in the corpus

Counterfactual inference for consumer choice across many product categories (2019, background): “is much less restrictive when applied at the individual level.12 The IIA property however has the advantage of providing substantial leeway to the econometrician in how to approach the estimation. As Train (2009, p. 53) points out, it is possible to generate consistent estimates of the model paramet”
Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis (2019, background): “Allenby-Train Procedure: (Train, 2003, p. 302-306): Assume that the prior k(θ) is the product of (a) multivariate normal density for μ with zero mean and sufficiently large variance so that the density is flat from a numerical perspective, and (b) an inverted Wishart for Ω with T degrees of freedom ”
A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings (2019, background): “15 Models are parametrized in the willingness-to-pay (WTP) space, which is defined as the ratio between 16 travel time (tti,n,t) and other level-of-service (LOS; Xi,n,t) coefficients and the travel cost (tci,n,t) coefficient 17 (e.g. Sillano and Ortúzar, 2005; Train and Weeks, 2005; Train, 2009), ma”
A Conceptual Framework for Ordered Logistic Regression Models (2009, methodology): “If the assumption of IIA is violated in multinomial logit, then other models such as multinomial probit or mixed logit may be more appropriate (Long 1997; Train 2003).”
Expanding Business-to-Business Customer Relationships: Modeling the Customer's Upgrade Decision (2008, methodology): “To account for the interdependency within each firm and to allow varying coefficients across firms, we estimate a random parameters model (Train 2003).”
Latent class model or mixed logit model? A comparison by transport mode choice data (2009, background): “…Louviere et al. (2000), McFadden and Train (2000), Bhat and Gossen (2004), Bishop and Provencher (2004), Greene (2003), Hensher et al. (2005), Train (2003), Train and Sandor (2004), Shen (2006), etc. 3Greene and Hensher (2003) mentioned that the LCM does not readily extend to…”
Random Regret Minimization: An Overview of Model Properties and Empirical Evidence (2012, background): “…maximization (RUM; McFadden, 1974) theory, and its subsequent development into a full-fledged econometric toolbox for modelling discrete (travel) choice behaviour (Ben-Akiva and Lerman, 1985; Train, 2003), there has been considerable interest in alternative behavioural discrete choice-model forms.”
Eliciting Risk and Time Preferences (2008, background): “So each 29Standard references include Gouriéroux and Monfort (1996), Train (2003), and Cameron and Trivedi (2005).”
Constructing Efficient Stated Choice Experimental Designs (2009, methodology): “Depending on the assumptions of εjsn, different model types result (e.g. MNL, NL or ML model, see e.g. Train, 2003).”