Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand (original) (raw)
Related papers
Applied Choice Analysis: A Primer
Journal of the American Statistical Association, 2007
Almost without exception, everything human beings undertake involves a choice. In recent years, there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer, the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation, and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit, and mixed logit. This primer will be an invaluable resource to students as well of immense value to consultants/professionals, researchers, and anyone else interested in choice analysis and modeling.
2008
Random utility models are widely used to analyze choice behaviour and predict choices among discrete alternatives in a given set. These models are based on the assumption that an individual's preference for the available alternatives can be described with a utility function and that the individual selects the alternative with the highest utility. The traditional formulation of logit models applied to transport demand assumes compensatory (indirect) utilities based on the trade-off between attributes.
Modeling methods for discrete choice analysis
1997
This paper introduces new forms, sampling and estimation approaches for discrete choice models. The new models include behavioral specifications of latent class choice models, multinomial probit, hybrid logit, and non-parametric methods. Recent contributions also include new specialized choice based sample designs that permit greater efficiency in data collection. Finally, the paper describes recent developments in the use of simulation methods for model estimation. These developments are designed to allow the applications of discrete choice models to a wider variety of discrete choice problems.
A latent class model for discrete choice analysis: contrasts with mixed logit
Transportation Research Part B: Methodological, 2003
The multinomial logit model (MNL) has for many years provided the fundamental platform for the analysis of discrete choice. The basic model's several shortcomings, most notably its inherent assumption of independence from irrelevant alternatives (IIA) have motivated researchers to develop a variety of alternative formulations. The mixed logit model stands as one of the most significant of these extensions. This paper proposes a semi-parametric extension of the MNL, based on the latent class formulation, which resembles the mixed logit model but which relaxes its requirement that the analyst makes specific assumptions about the distributions of parameters across individuals. An application of the model to the choice of long distance travel by three road types (2-lane, 4-lane without a median and 4lane with a median) by car in New Zealand is used to compare the MNL latent class model with mixed logit.
Representation of heteroskedasticity in discrete choice models
Transportation Research Part B: Methodological, 2000
The Multinomial Logit, discrete choice model of transport demand, has several restrictions when compared with the more general Multinomial Probit model. The most famous of these are that unobservable components of utilities should be mutually independent and homoskedastic. Correlation can be accommodated to a certain extent by the Hierarchical Logit model, but the problem of heteroskedasticity has received less attention in the literature. We investigate the consequences of disregarding heteroskedasticity, and make some comparisons between models that can and those that cannot represent it. These comparisons, which use synthetic data with known characteristics, are made in terms of parameter recovery and estimates of response to policy changes. The Multinomial Logit, Hierarchical Logit, Single Element Nested Logit, Heteroskedastic Extreme Value Logit and Multinomial Probit models are tested using data that are consistent with various error structures; only the last three can represent heteroskedasticity explicitly. Two dierent kinds of heteroskedasticity are analysed: between options and between observations. The results show that in the ®rst case, neither the Multinomial Logit nor the Single Element Nested Logit models can be used to estimate the response to policy changes accurately, but the Hierarchical Logit model performs surprisingly well. By contrast, in a certain case of discrete heteroskedasticity between observations, the simulation results show that in terms of response to policy variations the Multinomial Logit model performs as well as the theoretically correct Single Element Nested Logit and Multinomial Probit models. Furthermore, the Multinomial Logit Model recovered all parameters of the utility function accurately in this case. We conclude that the simpler members of the Logit family appear to be fairly robust with respect to some homoskedasticity violations, but that use of the more resource-intensive Multinomial Probit model is justi®ed for handling the case of heteroskedasticity between options. Ó
Comparing Choice Models Across Decision States: Some Preliminary Results
2005
During the decision-making process consumers pass through a series of stages or states as they progress from being aware of a new product category and/or offering to the final purchase choice. This paper presents preliminary results of a survey of 1495 people as they simulate the purchase of a DVD recorder. Using conditional logit models, we analyse the preferences of two groups of consumers, those who are “in the market” and those who are “not in the market”. The results show that the effects of sociodemographic and psychographic variables differ between the two groups but their preferences for particular attribute levels are similar.
Empirical test of a constrained choice discrete model: Mode choice in São Paulo, Brazil
Transportation Research Part B: Methodological, 1987
This paper examines the properties and empirically tests a model of discrete choice which incorporates probabilistic choice set generation. Denominated the Parametrized Logit Captivity (PLC) model, it is a generalization of the well-known "dogit" specification. The PLC model is shown to be theoretically and empirically more flexible than the latter. Work mode choice data collected in a 1977 O/D survey in SHo Paulo, Brazil, is used to obtain parameter estimates, as well as to evaluate consumer reaction to a series of perturbations in travel time, travel cost and income, for both the PLC and Multinomial Logit models. Comparisons between the two specifications are made in terms of statistical fit, rcasonableness of predictions and differences in predictions across models.
Behavioral and Descriptive Forms of Choice Models
2014
for helpful comments. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
Willingness-to-pay estimation with mixed logit models: some new evidence
Environment and Planning A, 2005
Since the dawn of discrete-choice modelling in the 1960s, when binary logit and probit models became useful tools to derive values of time, we have come a long wayöand increasingly faster in the last few years. We have seen almost three decades of unchecked rule by the multinomial (MNL) and nested logit (NL) models, with the more powerful and flexible multinomial probit (MNP) being left aside because of the difficulties involved with its use in real-life problems. Today, when computing power and better numerical techniques have made possible its use in practical applications, MNP has been overshadowed again by the equally flexible and/or powerful but less unyielding, mixed logit (ML) model. Both approaches have the ability to treat correlated and heteroscedastic alternatives, as well as random taste variations through the estimation of random rather than fixed parameters.