This paper examines the growth of a highway network based on the present and historical conditions of the network, traffic demand, demographic characteristics, project costs, and budget. The effects of expanding a link on its upstream and downstream neighbours, as well as on parallel links, are also considered. Data span two decades and consist of physical attributes of the network, their construction and expansion history, and traffic levels on each of the links. An observation of this research is that the rate of network expansion has decreased over time. The pattern of expansion for each type of highway was found to differ only marginally, indicating that the model estimated is reliable for general use. The models developed here have important implications for planning and forecasting.

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.

The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces consistent instrumental variables estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks and potentially endogenous regressors (prices). The nested fixed-point algorithm typically used for estimation is computationally intensive, largely because a system of market share equations must be repeatedly numerically inverted. We provide numerical theory results that characterize the properties of typical nested fixed-point implementations. We use these results to discuss several problems with typical computational implementations and, in particular, cases which can lead to incorrect parameter estimates. As a solution, we introduce a new computational formulation of the estimator that recasts estimation as a mathematical program with equilibrium constraints (MPEC). In many instances, MPEC is faster than the nested fixed point approach. It also avoids the numerical issues associated with nested inner loops. Several Monte Carlo experiments support our numerical concerns about NFP and the advantages of MPEC. We also discuss estimating static BLP using maximum likelihood instead of GMM. Finally, we show that MPEC is particularly attractive for forward-looking demand models where

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Currently, there is an important debate about the relative merits of models with discrete and continuous representations of consumer heterogeneity. In a recent JMR study, Andrews, Ansari, and Currim (2002; hereafter AAC) compared metric conjoint analysis models with discrete and continuous representations of heterogeneity and found no differences between the two models with respect to parameter recovery and prediction of ratings for holdout profiles. Models with continuous representations of heterogeneity fit the data better than models with discrete representations of heterogeneity. The goal of the current study is to compare the relative performance of logit choice models with discrete versus continuous representations of heterogeneity in terms of the accuracy of household-level parameters, fit, and forecasting accuracy. To accomplish this goal, the authors conduct an extensive simulation experiment with logit models in a scanner data context, using an experimental design based on AAC and other recent simulation studies. One of the main findings is that models with continuous and discrete representations of heterogeneity recover household-level parameter estimates and predict holdout choices about equally well except when the number of purchases per household is small, in which case the models with continuous representations perform very poorly. As in the AAC study, models with continuous representations of heterogeneity fit the data better.

Abstract: We develop a consumer-level model of vehicle choice to investigate the reasons behind the erosion of the U.S. automobile manufacturers ’ market share during the past decade. Our model accounts for the influence of vehicle attributes, brand loyalty, product line characteristics, and dealerships on choice. We find that nearly all of the loss in market share for U.S. manufacturers can be explained by changes in the basic attributes of a vehicle: price, size, power, operating cost, and body type. During the past decade, U.S. manufacturers have improved their vehicles ’ attributes but not as much as Japanese and European manufacturers have improved the attributes of their vehicles. 1

We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mixture models. We discuss three methods for proving identification of the distribution of heterogeneity for any given economic model. We prove the identification of the logit mixtures model, which, surprisingly given the wide use of this model over the last 30 years, is a new result. We also derive our estimator’s non-standard asymptotic distribution and demonstrate its excellent small sample properties in a Monte Carlo. The estimator we propose can be extended to allow for endogenous prices. The estimator can also be used to reduce the computational burden of nested fixed point methods for complex models like dynamic programming discrete choice.

Preliminary draft. Please do not quote. Comments are welcome. This paper quantifies the degree of competition and spatial differentiation across different retail channels by exploiting a unique dataset that describes a consumer’s choice of store, product of purchase, item price, and demographics. For each household, I collect information on the location and distance of nearby stores. Then I estimate a consumer’s choice of retailer in the sales market for DVDs among online, mass merchant, electronics, video specialty, and music stores. Using a discrete choice model, I allow for unobserved heterogeneity in preferences for store types and disutility of travel. A consumer’s traveling cost varies by income, and substitution occurs proportionately more among stores of the same type. Conditional on price and distance, the average consumer still prefers Wal-Mart over most other stores. In addition, consumers ’ shopping patterns across store types vary significantly by gender, education, and the presence of children. * I would like to thank Glenn Ellison, Nancy Rose, Paul Joskow, and Sara Fisher Ellison for advice and helpful suggestions. This paper has benefited from conversations with Emek Basker, Melissa Boyle, Norma Coe, Jerry

In a famous episode of financial history which lasted over eight years, the market for the future on the Bund moved entirely from LIFFE, the incumbent London-based derivatives exchange, to DTB, the entering Frankfurt-based exchange. This paper studies the determinants of traders’ exchange choice, using a novel panel dataset that contains individual trading firms’ membership status at each exchange together with other firms characteristics, and pricing, marketing and product portfolio strategies by each exchange. Our data allows us to evaluate different sources of heterogeneity among trading firms and thus, to distinguish between different explanations for the observed phenomenon. The story the data tell is one of horizontal differentiation induced artifically by national access barriers that came down after deregulation in Europe and in the US, with secondary played by endogenous vertical differentiation due to liquidity and exchange scope effects.