Testing the link specification in binary choice models. A semiparametric approach.

Doutoramento em Matemática.

Subjective health assessments and active labor market participation of older men: evidence from a semiparametric binary choice model with nonadditive correlated individual-specific effects

We use panel data from the U. S. Health and Retirement Study, 1992-2002, to estimate the effect of self-assessed health limitations on the active labor market participation of older men. Self-assessments of health are likely to be endogenous to labor supply due to justification bias and individual-specific heterogeneity in subjective evaluations. We address both concerns. We propose a semiparametric binary choice procedure that incorporates nonadditive correlated individual-specific effects. Our estimation strategy identifies and estimates the average partial effects of health and functioning on labor market participation. The results indicate that poor health plays a major role in labor market exit decisions.

Take-the-best and other simple strategies: Why and when they work 'well' in binary choice

The effectiveness of decision rules depends on characteristics of bothrules and environments. A theoretical analysis of environments specifiesthe relative predictive accuracies of the lexicographic rule 'take-the-best'(TTB) and other simple strategies for binary choice. We identify threefactors: how the environment weights variables; characteristics of choicesets; and error. For cases involving from three to five binary cues, TTBis effective across many environments. However, hybrids of equal weights(EW) and TTB models are more effective as environments become morecompensatory. In the presence of error, TTB and similar models do not predictmuch better than a naïve model that exploits dominance. We emphasizepsychological implications and the need for more complete theories of theenvironment that include the role of error.

Ignoring information in binary choice with continuous variables: When is less 'more'?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when one-reason decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect less to be more.

Eliciting consumers preferences using stated preference discrete choice models: Contingent ranking versus choice experiment

The aim of this paper is twofold: firstly, to carry out a theoreticalreview of the most recent stated preference techniques used foreliciting consumers preferences and, secondly, to compare the empiricalresults of two dierent stated preference discrete choice approaches.They dier in the measurement scale for the dependent variable and,therefore, in the estimation method, despite both using a multinomiallogit. One of the approaches uses a complete ranking of full-profiles(contingent ranking), that is, individuals must rank a set ofalternatives from the most to the least preferred, and the other usesa first-choice rule in which individuals must select the most preferredoption from a choice set (choice experiment). From the results werealize how important the measurement scale for the dependent variablebecomes and, to what extent, procedure invariance is satisfied.

Persuasion, binary choice, and the costs of dishonesty

We study the strategic interaction between a decision maker who needs to take a binary decision but is uncertain about relevant facts and an informed expert who can send a message to the decision maker but has a preference over the decision.We show that the probability that the expert can persuade the decision maker to take the expert's preferred decision is a hump-shaped function of his costs of sending dishonest messages.

Consumer valuation of energy-saving features of residential air conditioners with hedonic and choice models

The promotion of energy-efficient appliances is necessary to reduce the energetic and environmental burden of the household sector. However, many studies have reported that a typical consumer underestimates the benefits of energy-saving investment on the purchase of household electric appliances. To analyze this energy-efficiency gap problem, many scholars have estimated implicit discount rates that consumers use for energy-consuming durables. Although both hedonic and choice models have been used in previous studies, a comparison between two models has not yet been done. This study uses point of sale data about Japanese residential air conditioners and estimates implicit discounts rates with both hedonic and choice models. Both models demonstrate that a typical consumer underinvests in energy efficiency. Although choice models estimate a lower implicit discount rate than hedonic models, the latter models estimate the values of other product characteristics more consistently than choice models.

Analysis of preferences for freight transport using advanced choice models

Programa de doctorado: Perspectivas científicas sobre el turismo y la dirección de empresas turísticas

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Optimization of traffic signal controls within equilibrium route choice models : final report to Illinois Department of Transpotation/

Cover title.

Subregional route choice models with link travel times reflecting intersection flows : final report to Illinois Department of Transportation /

"Report no.: FHWA/IL/RC-008."--Documentation page.

A note on maximum likelihood estimation of discrete choice models from the 1978 survey of disability and work /

"November 1982."

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.