Standard Discrete Choice Research Articles

Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling

Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present.

Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life

End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.

Copula-Based Random Effects Models for Clustered Data

Sorting and spillovers can create correlation in individual outcomes. In this situation, standard discrete choice estimators cannot consistently estimate the probability of joint and conditional events, and alternative estimators can yield incoherent statistical models or intractable estimators. I propose a random effects estimator that models the dependence among the unobserved heterogeneity of individuals in the same cluster using a parametric copula. This estimator makes it possible to compute joint and conditional probabilities of the outcome variable, and is statistically coherent. I describe its properties, establishing its efficiency relative to standard random effects estimators, and propose a specification test for the copula. The likelihood function for each cluster is an integral whose dimension equals the size of the cluster, which may require high-dimensional numerical integration. To overcome the curse of dimensionality from which methods like Monte Carlo integration suffer, I propose an algorithm that works for Archimedean copulas. I illustrate this approach by analysing labour supply in married couples.

Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition

This article develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the article first develops a general framework for estimating and solving dynamic discrete choice models in continuous time that is computationally light and readily applicable to dynamic games. In the proposed framework, players face a standard dynamic discrete choice problem at decision times that occur stochastically. The resulting stochastic-sequential structure naturally admits the use of conditional choice probability methods for estimation and makes it possible to compute counterfactual simulations for relatively high-dimensional games. The model and method are applied to the retail grocery industry, into which Walmart began rapidly expanding in the early 1990s, eventually attaining a dominant position. We find that Walmart's expansion into groceries came mostly at the expense of the large incumbent supermarket chains, rather than the single-store outlets that bore the brunt of its earlier conquest of the broader general merchandise sector. Instead, we find that independent grocers actually thrive when Walmart enters, leading to an overall reduction in market concentration. These competitive effects are strongest in larger markets and those into which Walmart expanded most rapidly, suggesting a diminishing role of scale and a greater emphasis on differentiation in this previously mature industry.

A two‐sided matching approach for partner selection and assessing complementarities in partners' attributes in inter‐firm alliances

Research summary: Strategic alliances are undertaken to create value through complementarities of resources and capabilities of the partner firms. This paper uses a recently developed estimator of matching games, i.e., the maximum score estimator, to advance strategic management research on partner selection in strategic alliances, with a focus on the formation of research alliances in the biopharmaceutical industry. We contribute to the literature in three ways. First, we develop a matching framework to study strategic alliances, taking a market perspective that explicitly incorporates key features of alliance formation: two‐sided decision making; quest for complementarities between indivisible and heterogeneous partner attributes; and competition on each side of the market for partners on the other side of the market. Second, we assess the relative performance of the maximum score and standard discrete choice estimators by performing simulations based on known functional relationships in various matching scenarios. Third, within the context of biopharmaceutical research alliances, we hypothesize and find support using the maximum score estimator for complementarity in partner size and in upstream research capabilities.Managerial summary: A critical question facing managers who seek to benefit from strategic alliances is “whom to ally with”. Typically, each party seeks a partner whose attributes reinforce their own. This paper explains that the interaction of these preferences leads to a market-wide sorting of alliance partners. Since the value created by an alliance is driven by attributes of all alliance partners, firms cannot successfully bid financial resources to get access to their most preferred partner. Instead, managers need to understand the market-wide competition and invest in the “right” mixture of attributes to make their firm more attractive in the market for alliance partners. Using this framework, we highlight firms' sizes and research capabilities as two drivers of partner selection and sorting in bio-pharmaceutical research alliances. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches

Integrated choice and latent variable (ICLV) model incorporates latent factors into standard discrete choice model with aim to provide greater explanatory power. Using simulated datasets, this study makes a comparison among three estimation approaches corresponding to the sequential approach and two simultaneous approaches including the maximum simulated likelihood with GHK estimator and maximum approximate composite marginal likelihood (MACML) approach, to evaluate their abilities to recover the underlying parameters of multinomial probit-kernel ICLV model. The results show that both simultaneous approaches outperform the sequential approach in terms of estimates accuracy and efficiency irrespective of the sample sizes, and the MACML approach is the most preferable due to its best performance on recovering true values of parameters with relatively small standard errors, especially when the sample size is large enough.

Persuasive Puffery

Sellers often make claims about product strengths without providing evidence. Even though such claims are mere puffery, we show that they can be credible because talking up any one strength comes at the implicit trade-off of not talking up another potential strength. Puffery pulls in some buyers who value product attributes that are talked up or emphasized while pushing away other buyers who infer that the attributes they value are relative weaknesses. When the initial probability of making a sale is low, there are more potential buyers to pull in than to push away, so puffery is persuasive overall. This persuasiveness requires that buyers have some privacy about their preferences so that the seller does not completely pander to them. More generally, the results show how comparative cheap talk by an expert to a decision maker can be credible and persuasive in standard discrete choice models used throughout marketing, economics, and other disciplines.

A specification test for discrete choice models

In standard discrete choice models, adding options cannot increase the choice probability of an existing alternative. We use this observation to construct a simple nonparametric specification test by exploiting variation in the choice sets individuals face. We use a multiple testing procedure to determine the particular kind of choice sets that produce violations. We apply these tests to the 1896 US House of Representatives election and reject commonly used discrete choice voting models.

Two New Prediction-Driven Approaches to Discrete Choice Prediction

The ability to predict consumer choices is essential in understanding the demand structure of products and services. Typical discrete choice models that are targeted at providing an understanding of the behavioral process leading to choice outcomes are developed around two main assumptions: the existence of a utility function that represents the preferences over a choice set and the relatively simple and interpretable functional form for the utility function with respect to attributes of alternatives and decision makers. These assumptions lead to models that can be easily interpreted to provide insights into the effects of individual variables, such as price and promotion, on consumer choices. However, these restrictive assumptions might impede the ability of such theory-driven models to deliver accurate predictions and forecasts. In this article, we develop novel approaches targeted at providing more accurate choice predictions. Specifically, we propose two prediction-driven approaches: pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We compare our proposed approaches with a multiclass classification approach, as well as a standard discrete choice model. Our empirical results show that the proposed approaches achieved significantly higher choice prediction accuracy.

Taking account of the role of safety on vehicle choice using a new generation of discrete choice models

In attitudinal studies safety often appears as an important attribute desired by consumers when buying a new car ( Ben-Akiva and Lerman, 1985). However, economic models of vehicle choice usually neglect the role of safety. On the one hand, to capture the qualitative nature of safety and variables related to safety one should consider safety as an underlying construct in a context of latent variable models. The problem is that psychometric models that make use of latent variables do not necessarily provide a complete understanding of agent behavior and may lead to poor predictive power. On the other hand, discrete choice models – although a powerful tool to explain decision making based on utility maximization behavior – fail to include qualitative factors as explanatory variables of the decision process. In this paper, we explore how to model safety through a new generation of discrete choice models which simultaneously consider both a standard discrete choice model and latent causal variables. Using stated preference data concerning purchase intentions of low-emission vehicles in Canada, we test a hybrid choice model to explain consumers’ preferences for safety. Based on the results as well as on the hybrid choice modeling approach, we outline a general framework for the correct modeling of the adoption of safer vehicles and appreciation of safety equipment.

Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model

In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, we show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. The second is the capacity of HCMs to adapt to practical situations. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discrete choice models because it not only incorporates pro-environmental preferences but also provides tools to build a profile of environmentally-conscious consumers.

CHOICE AND NON-COORDINATION BEHAVIOR IN A GLOBAL AND LOCAL INFORMATION SETTING: A COMPUTATIONAL APPROACH

This paper studies conformity effects on individual choice when both local and global information are present. A standard discrete choice model that incorporates the social interaction effect to assign choice probabilities to agents is studied. The model is analyzed by using computational simulations. Agents are dispersed in a two dimensional toroidal lattice and they can gather information either from their von Neumann neighbors or from the whole community of agents. Agent heterogeneity is introduced through diversity in private incentives among agents. The main results of the simulations show that the effects of global and local information can produce conflicting informational data streams in agents, making it likely that unstable and volatile aggregate choice emerges. Likewise, the results indicate that the interaction between global and local information affects the shape of the distribution of the size of informational cascades.
 
 RESUMEN
 Esta investigación analiza los efectos de conformidad en las decisiones de los individuos en un contexto en donde los agentes económicos acceden a información global y local. Un modelo de decisión discreta que incorpora el efecto de interacción social es analizado por medio de simulaciones computacionales. Los agentes bajo estudio están dispersos en una cuadrícula de dos dimensiones en donde recolectan información ya sea a partir de sus vecinos von Neumann o de la comunidad entera de agentes. La heterogeneidad es introducida a partir de la diversidad en los incentivos privados de los agentes. Los principales resultados de las simulaciones indican que los efectos de la información global y local pueden producir flujos informativos conflictivos para la toma de decisiones, de tal forma que el comportamiento agregado de decisión de los agentes emerge como volátil e inestable. Asimismo, los resultados indican que la interacción entre información global y local afecta la forma de la distribución del tamaño de las cascadas informativas.

Estimation of Dynamic Discrete Choice Models in Continuous Time

This paper develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the paper first develops a general framework for estimating and solving dynamic discrete choice models in continuous time that is computationally light and readily applicable to dynamic games. In the proposed framework, players face a standard dynamic discrete choice problem at decision times that occur stochastically. The resulting stochastic-sequential structure naturally admits the use of CCP methods for estimation and makes it possible to compute counterfactual simulations for relatively high-dimensional games. The model and method are applied to the retail grocery industry, into which Wal-Mart began rapidly expanding in the early 1990s, eventually attaining a dominant position. We find that Wal-Mart’s expansion into groceries came mostly at the expense of the large incumbent supermarket chains, rather than the single-store outlets that bore the brunt of its earlier conquest of the broader general merchandise sector. Instead, we find that independent grocers actually thrive when Wal-Mart enters, leading to an overall reduction in market concentration. These competitive effects are strongest in larger markets and those into which Wal-Mart expanded most rapidly, suggesting a diminishing role of scale and a greater emphasis on differentiation in this previously mature industry.

Random covariance heterogeneity in discrete choice models

In this paper, we extend the standard discrete choice modelling framework by allowing for random variations in the substitution patterns between alternatives across respondents, leading to increased model flexibility. The paper shows how such a Mixed Covariance model can be specified either with purely random variation or with a mixture of random and deterministic variation. Additionally, the model can be based on an underlying GEV or ECL structure. Finally, the model can be specified as a continuous mixture or as a discrete mixture. An application on Stated Preference data for the choice of departure time and travel mode shows that important gains in model performance can be obtained by allowing for random covariance heterogeneity. Furthermore, the approach leads to significant differences in the implied willingness to pay measures, and the substitution patterns between alternatives.

Identification and Estimation of a Discrete Game of Complete Information

We discuss the identification and estimation of discrete games of complete information. Following Bresnahan and Reiss (1990, 1991), a discrete game is a generalization of a standard discrete choice model where utility depends on the actions of other players. Using recent algorithms to compute all of the Nash equilibria to a game, we propose simulation-based estimators for static, discrete games. We demonstrate that the model is identified under weak functional form assumptions using exclusion restrictions and an identification at infinity approach. Monte Carlo evidence demonstrates that the estimator can perform well in moderately sized samples. As an application, we study entry decisions by construction contractors to bid on highway projects in California. We find that an equilibrium is more likely to be observed if it maximizes joint profits, has a higher Nash product, uses mixed strategies, and is not Pareto dominated by another equilibrium.

Labour Market Responses of Survival Pensioners: Estimating a Labour Supply Model and Predicting the Effect of the Reform

In this paper we use a sample of administrative data coming from the 'Dataware-house labour market and social protection' and the microsimulation model MIMOSIS to assess the labour supply effects of a reform of the rules for cumulating labour income with survival pension as proposed in the Generations Pact. In a first step we estimate a standard discrete choice labour supply model for several sub groups. Subsequently we model the proposed reform in the tax and benefit rules and we predict the change in desired labour supply of the targeted group. The reform has a significant positive effect on the labour supply of widows, but the effects are quite weak amongst the survivor pensioner with very low benefit, i.e. the group that was originally thought to benefit the most from the reform.

Product Differentiation and Mergers in the Carbonated Soft Drink Industry

I simulate the competitive impact of several soft drink mergers from the 1980s on equilibrium prices and quantities. An unusual feature of soft drink demand is that, at the individual purchase level, households regularly select a variety of soft drink products. Specifically, on a given trip households may select multiple soft drink products and multiple units of each. A concern is that using a standard discrete choice model that assumes single unit purchases may understate the price elasticity of demand. To model the sophisticated choice behavior generating this multiple discreteness, I use a household‐level scanner data set. Market demand is then computed by aggregating the household estimates. Combining the aggregate demand estimates with a model of static oligopoly, I then run the merger simulations. Despite moderate price increases, I find substantial welfare losses from the proposed merger between Coca‐Cola and Dr. Pepper. I also find large price increases and corresponding welfare losses from the proposed merger between Pepsi and 7 UP and, more notably, between Coca‐Cola and Pepsi.

A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions

Several consumer demand choices are characterized by the choice of multiple alternatives simultaneously. An example of such a choice situation in activity-travel analysis is the type of discretionary (or leisure) activity to participate in and the duration of time investment of the participation. In this context, within a given temporal period (say a day or a week), an individual may decide to participate in multiple types of activities (for example, in-home social activities, out-of-home social activities, in-home recreational activities, out-of-home recreational activities, and out-of-home non-maintenance shopping activities). In this paper, we derive and formulate a utility theory-based model for discrete/continuous choice that assumes diminishing marginal utility as the level of consumption of any particular alternative increases (i.e., satiation). This assumption yields a multiple discreteness model (i.e., choice of multiple alternatives can occur simultaneously). This is in contrast to the standard discrete choice model that is based on assuming the absence of any diminishing marginal utility as the level of consumption of any alternative increases (i.e., no satiation), leading to the case of strictly single discreteness. The econometric model formulated here, which we refer to as the multiple discrete–continuous extreme value (MDCEV) model, has a surprisingly simple and elegant closed form expression for the discrete–continuous probability of not consuming certain alternatives and consuming given levels of the remaining alternatives. To our knowledge, we are the first to develop such a simple and powerful closed-form model for multiple discreteness in the literature. This formulation should constitute an important milestone in the area of multiple discreteness, just as the multinomial logit (MNL) represented an important milestone in the area of single discreteness. Further, the MDCEV model formulated here has the appealing property that it collapses to the familiar multinomial logit (MNL) choice model in the case of single discreteness. Finally, heteroscedasticity and/or correlation in unobserved characteristics affecting the demand of different alternatives can be easily incorporated within the MDCEV model framework using a mixing approach. The MDCEV model and its mixed variant are applied to analyze time-use allocation decisions among a variety of discretionary activities on weekends using data from the 2000 San Francisco Bay Area survey.

Product Differentiation and Mergers in the Carbonated Soft Drink Industry

I simulate the competitive impact of several soft drink mergers from the 1980s on equilibrium prices and quantities. An unusual feature of soft drink demand is that, at the individual purchase level, households regularly select a variety of soft drink products. Specifically, on a given trip households may select multiple soft drink products and multiple units of each. A concern is that using a standard discrete choice model that assumes single unit purchases may understate the price elasticity of demand. To model the sophisticated choice behavior generating this multiple discreteness, I use household-level scanner data set. Market demand is then computed by aggregating the household estimates. Combining the aggregate demand estimates with a model of static oligopoly, I then run the merger simulations. Consistent with the arguments of Coca-Cola, we find that only the merger between Coca-Cola and Pepsi leads to substantial price increases and corresponding welfare losses.

Migration and implicit amenity markets: does incomplete compensation matter?

We first develop an empirical approach for generating measures of wage over or under compensation (incomplete compensation) for location attributes. We then devise a method to test whether migration is influenced by incomplete compensation in wages for location characteristics. An intercity wage regression is estimated where fixed effects capture the impact of site characteristics on wages. We then regress the fixed effects on a comprehensive vector of site attributes, where the residuals from this stage capture incomplete compensation in wages. The derived measures of incomplete compensation are included in a standard microdata-based discrete choice model of migration. The results suggest that incomplete wage compensation for site characteristics matters in migration decisions, and the findings are consistent with tendencies toward spatial equilibrium in the distribution of population. Copyright 2003, Oxford University Press.