01478780

Component

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.

This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting.

01470560

13-5005

English

25p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

References; Tables

Choice models; Logits; Multinomial logits; Simulation; Statistical sampling; Variables

Data and Information Technology; Highways; Planning and Forecasting; I21: Planning of Transport Infrastructure; I70: Traffic and Transport

Transportation Research Board Annual Meeting 2013 Paper #13-5005

TRIS, TRB, ATRI

Feb 5 2013 12:57PM