2013 Session: 324

• Sampling of Alternatives in Random Regret Minimization Models
  Abstract: We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Random Regret Minimization models. Our method is an extension of previous results for Logit and MEV models. We illustrate the methodology using Monte Carlo experimentation. Experiments show that the proposed methodology is practical, that it outperforms the uncorrected model, and that it yields acceptable results.
  Authors: Guevara, C. Angelo; Chorus, Caspar; Ben-Akiva, Moshe E.
  Authors: Guevara, C. Angelo; Chorus, Caspar; Ben-Akiva, Moshe E.
  Year: 2013
  Document Type: Paper
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-2063
• Generalized Multinomial Logit Model: Misinterpreting Scale and Preference Heterogeneity in Discrete Choice Models or Untangling the Un-Untanglable?
  Abstract: Recently published papers dealing with issues of scale and preference heterogeneity are having a significant impact on the choice modeling community. In particular, the generalized multinomial logit model (GMNL) is now being widely promoted as the model of choice in many discipline areas given its purported ability to separately identify scale and preference heterogeneity. The purpose of this paper is to firstly discuss a number of issues related to the estimation of the GMNL model. The second objective of the paper is to argue that the GMNL model does not in fact untangle scale and preference heterogeneity as has been reported and that the outputs derived from the model have been misinterpreted. We further argue that the model is not a generalised version of the mixed multinomial logit model, but in fact is a mixed multinomial logit with more flexible, but still restrictive, mixtures of distributions that do not necessarily equate to an ability to capture scale heterogeneity. We finally discuss the only theoretical circumstance under which random scale and preference can be separately identified within the logit family of models.
  Authors: Rose, John Matthew; Hess, Stephane; Greene, William H.; Hensher, David A.
  Authors: Rose, John Matthew; Hess, Stephane; Greene, William H.; Hensher, David A.
  Year: 2013
  Document Type: Paper
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-2297
• Are Nontraders the Achilles Heel of the Mixed Logit Model?
  Abstract: While the mixed multinomial logit model is quickly becoming analysts’ model structure of choice, this paper demonstrates that some issues arise in retrieving intuitive willingness-to-pay estimates when there are small percentages of respondents exhibiting extreme, non-trading behavior. This is shown to be particularly true for the log-normal distribution which is quite commonly used in practice because of its ability to constrain taste heterogeneity to one side of 0. We first use the Danish value of time stated choice dataset to demonstrate the impact of non-trading on mean value of travel time (VTT) estimates when the analyst assumes VTT is normal and log-normally distributed. We then move to a synthetic approach to further illustrate the particular issues with the log-normal distribution.
  Authors: Dumont, Jeffrey; Hess, Stephane; Daly, Andrew
  Authors: Dumont, Jeffrey; Hess, Stephane; Daly, Andrew
  Year: 2013
  Document Type: Paper
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-3894
• Evaluating Alternate Discrete Choice Frameworks for Modeling Ordinal Discrete Variables
  Abstract: Discrete choice models in their broadest sense can be characterized as ordered and unordered response frameworks. The ordered response frameworks are suited for examining discrete variables that are ordinal in nature while the unordered response frameworks are applicable to analyzing all discrete variables. The applicability of the two frameworks for analyzing ordinal discrete variables has evoked considerable debate on using the appropriate choice model for analysis. The ordered response models explicitly recognize the inherent ordering within the decision variable whereas the unordered response models neglect the ordering or require artificial constructs to consider the ordering. On the other hand, the traditional ordered response models restrict the impact of exogenous variables on the choice process to be same across all alternatives while the unordered response models allow the model parameters to vary across alternatives. Another concern with the ordered response framework is in the context of modeling datasets that might be affected by under reporting. There are two aspects that need to be considered: (1) the model framework that offers superior statistical fit (and thereby behavioral interpretability) and (2) performance in the presence of under reported data.The objective of the current study is to investigate the performance of the ordered and unordered response frameworks at a fundamental level. Towards this end, we undertake a comparison of the alternative frameworks by estimating ordered and unordered response models using data generated through ordered, unordered data and a combination of ordered and unordered data generation processes. We also examine the influence of aggregate sample shares on the appropriateness of the modeling framework. Rather than be limited by the aggregate sample shares in an empirical dataset, simulation allows us to explore the influence of a broad spectrum of sample shares on the performance of ordered and unordered frameworks. We extend the data generation process based analysis to under reported data and compare the performance of the ordered and unordered response frameworks. Finally, based on these simulation exercises, we provide a discussion of the merits of the different approaches. The results clearly highlight the emergence of the generalized ordered logit model as a true competitor (if not a better model) to the multinomial logit model for ordinal discrete variables.
  Authors: Eluru, Naveen
  Authors: Eluru, Naveen
  Year: 2013
  Document Type: Paper
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-5005
• Sampling of Alternatives in Random Regret Minimization Models
  Authors: Chorus, Caspar
  Authors: Chorus, Caspar
  Year: 2013
  Document Type: Presentation
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-2063
• Generalized Multinomial Logit Model: Misinterpreting Scale and Preference Heterogeneity in Discrete Choice Models or Untangling the Un-Untanglable?
  Authors: Hess, Stephane
  Authors: Hess, Stephane
  Year: 2013
  Document Type: Presentation
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-2297
• Evaluating Alternate Discrete Choice Frameworks for Modeling Ordinal Discrete Variables
  Authors: Eluru, Naveen
  Authors: Eluru, Naveen
  Year: 2013
  Document Type: Presentation
  Subject: Planning and Forecasting
  Session: 324
  Paper Number: 13-5005