This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices ...that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
We develop a consumer-level model of vehicle choice to shed light on the erosion of the U.S. automobile manufacturers' market share during the past decade. We examine the influence of vehicle ...attributes, brand loyalty, product line characteristics, and dealerships. We find that nearly all of the loss in market share for U.S. manufacturers can be explained by changes in basic vehicle attributes, namely: price, size, power, operating cost, transmission type, reliability, and body type. U.S. manufacturers have improved their vehicles' attributes but not as much as Japanese and European manufacturers have improved the attributes of their vehicles.
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Quasi-random number sequences have been used extensively for many years in the simulation of integrals that do not have a closed-form expression, such as Mixed Logit and Multinomial Probit choice ...probabilities. Halton sequences are one example of such quasi-random number sequences, and various types of Halton sequences, including standard, scrambled, and shuffled versions, have been proposed and tested in the context of travel demand modeling. In this paper, we propose an alternative to Halton sequences, based on an adapted version of Latin Hypercube Sampling. These alternative sequences, like scrambled and shuffled Halton sequences, avoid the undesirable correlation patterns that arise in standard Halton sequences. However, they are easier to create than scrambled or shuffled Halton sequences. They also provide more uniform coverage in each dimension than any of the Halton sequences. A detailed analysis, using a 16-dimensional Mixed Logit model for choice between alternative-fuelled vehicles in California, was conducted to compare the performance of the different types of draws. The analysis shows that, in this application, the Modified Latin Hypercube Sampling (MLHS) outperforms each type of Halton sequence. This greater accuracy combined with the greater simplicity make the MLHS method an appealing approach for simulation of travel demand models and simulation-based models in general.
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► Recovery of correct patterns of heterogeneity can be problematic. ► Problems arise especially in the presence of intra-respondent heterogeneity. ► Sample size requirements of mixed logit are more ...substantial than commonly assumed. ► Evidence that computationally attractive approximation techniques lead to inferior results. ► Level of heterogeneity has a major impact on ability of different methods to recover true patterns.
Most applications of discrete choice models in transportation now utilise a random coefficient specification, such as mixed logit, to represent taste heterogeneity. However, little is known about the ability of these models to capture the heterogeneity in finite samples (as opposed to asymptotically). Also, due to the computational intensity of the standard estimation procedures, several alternative, less demanding methods have been proposed, and yet the relative accuracy of these methods has not been investigated. This is especially true in the context of work looking at joint inter-respondent and intra-respondent variation. This paper presents an overview of the various different estimators, gives insights into some of the theoretical properties, and analyses their
performance in a large scale study on simulated data. In particular, we specify 31 different forms of heterogeneity, with multiple versions of each dataset, and with results from over 16,000 mixed logit estimation runs. The findings suggest that variation in tastes over consumers is captured by all the methods, including the simpler versions, at least when sample size is sufficiently large. When tastes vary over choice situations for each consumer, as well as over consumers, the ability of the methods to capture and differentiate the two sources of heterogeneity becomes more tenuous. Only the most computationally intensive approach is able to capture adequately the two sources of variation, but at the cost of very high run times. Our results highlight the difficulty of retrieving taste heterogeneity with only cross-sectional data, providing further evidence of the benefits of repeated choice data. Our findings also suggest that the data requirements of random coefficients models may be more substantial than is commonly assumed, further reinforcing concerns about small sample issues.
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This paper describes and implements three computationally attractive procedures for nonparametric estimation of mixing distributions in discrete choice models. The procedures are specic types of the ...well known EM (Expectation-Maximization) algorithm based on three dierent ways of approximating the mixing distribution nonparametrically: (1) a discrete distribution with mass points and frequencies treated as parameters, (2) a discrete mixture of continuous distributions, with the moments and weight for each distribution treated as parameters, and (3) a discrete distribution with fixed mass points whose frequencies are treated as parameters. The methods are illustrated with a mixed logit model of households' choices among alternative-fueled vehicles.
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We estimate random-parameter logit models of anglers' choices of fishing sites. The models generalize logit by allowing coefficients to vary randomly over anglers rather than being fixed. The models ...do not exhibit the restrictive "independence from irrelevant alternatives property" of logit and can represent any substitution pattern. Estimation explicitly accounts for the fact that the variation in coefficients over anglers induces correlation in unobserved utility over trips by the same angler. Willingness to pay for improved fish stock and the value to anglers of specific sites are calculated from the models and compared with the estimates obtained from a standard logit model.
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We examine small/medium commercial and industrial customers' choices among energy suppliers in conjoint-type experiments. The distribution of customers' willingness to pay is estimated for more than ...40 attributes of suppliers, including sign-up bonuses, amount and type of renewables, billing options, bundling with other services, reductions in voltage fluctuations, and charitable contributions. These estimates provide guidance for suppliers in designing service options and to economists in anticipating the services that will be offered in competitive retail energy markets.
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This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Each of the major models is covered including logit, generalized ...extreme value, or GEV, probit, and mixed logit, plus a variety of specifications that build on these basics.
Monte Carlo analysis of SP-off-RP data Train, Kenneth E; Wilson, Wesley W
Journal of choice modelling,
2009, 2009-00-00, Volume:
2, Issue:
1
Journal Article
Peer reviewed
Open access
SP-off-RP questions are a recent innovation in choice modelling that solicits information from respondents in a different way than standard stated-preference (SP) experiments. In particular, the ...alternatives and choice of a respondent in a real-world setting are observed, and the respondent is asked whether he/she would choose the same alternative or switch to another alternative if the attributes of the chosen alternative were less desirable in ways specified by the researcher and/or the attributes of non-chosen alternatives were more desirable in specified ways. This construction, called stated-preference off revealed-preference (SP-off-RP), is intended to increase the realism of the stated-preference task, relative to standard SP exercises, but creates endogeneity. In this paper, we present a series of Monte Carlo exercises that explore estimation on this type of data, using an estimator that accounts for the endogeneity. The results indicate that, when the variance in the processing error by respondents is the same for SP-off-RP data as for standard SP data, the two solicitation methods provide about the same level of efficiency in estimation, even though the SP-off-RP data contain endogeneity that the estimator must handle while the SP data do not involve endogeneity. For both solicitation methods, efficiency rises, as expected, as the variance of the processing error decreases. These results imply that, if respondents are able to answer SP-off-RP questions more accurately than standard SP questions (and hence have lower variance of processing error), then SP-off-RP data are more efficient that standard SP data. This implication needs to be viewed cautiously, since (i) the actual processing error for each solicitation method is not measured in the current study, and (ii) the results are for the specific data generation processes that are used in the Monte Carlo exercises.
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