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We estimate random-parameter logit models of anglers' choice of fishing site. 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.

ome detail more recent developments in the economic theory of choice, and modifications to this theory that are being forced by experimental evidence from cognitive psychology. I will close with a survey of statistical methods that have developed as part of the research program on economic choice behavior. Science is a cooperative enterprise, and my work on choice behavior reflects not only my own ideas, but the results of exchange and collaboration with many other scholars. 1 First, of course, is my co-laureate James Heckman, who among his many contributions pioneered the important area of dynamic discrete choice analysis. Nine other individuals who played a major role in channeling microeconometrics and choice theory toward their modern forms, and had a particularly important influence on my own work, are Zvi Griliches, L.L. Thurstone, Jacob Marschak, Duncan Luce, Danny Kahneman, Amos Tversky, Moshe Ben-Akiva, Charles Manski, and Kenneth Train. A gallery of their p

Many important economic questions hinge on the extent to which new goods either crowd out or complement consumption of existing products. Recent methods for studying new goods are based on demand models that rule out complementarity by assumption, so their applicability to these questions has been limited. I develop a new model that relaxes this restriction, and use it to study the specific case of competition between print and online newspapers. Using new micro data from the Washington DC market, I show that the major print and online papers appear to be strong complements in the raw data, but that this is an artifact of unobserved consumer heterogeneity. I estimate that the online paper reduced print readership by 27,000 per day, at a cost of $5.5 million per year in lost print profits. I find that online news has provided substantial welfare benefits to consumers and that charging positive online prices is unlikely to substantially increase firm profits. JEL classification:C25,L82

The quantal response equilibrium (QRE) notion of Richard D. McKelvey and Thomas R. Palfrey (1995) has recently attracted considerable attention, due in part to its widely documented ability to rationalize observed behavior in games played by experimental subjects. However, even with strong apriorirestrictions on unobservables, QRE imposes no falsifiable restrictions: it can rationalize any distribution of behavior in any normal form game. After demonstrating this, we discuss several approaches to testing QRE under additional maintained assumptions. (JEL C72, C52, C90) The quantal response equilibrium (QRE) notion of McKelvey and Palfrey (1995) can be viewed as an extension of standard random utility models of discrete (“quantal”) choice to strategic settings, or as a generalization of Nash equilibrium that allows noisy optimizing behavior while maintaining the internal consistency of rational expectations. Formally, QRE is based on the introduction of random perturbations to the payoffs associated with each action a player can take. 1 Realizations of these perturbations affect which action is the best response to the equilibrium distribution of opponents ’ behavior. 1We give a more complete discussion in the following section. The literature has considered generalizations of the QRE to extensive form games (McKelvey and Palfrey (1998)) and games with continuous strategy

This paper tests different models of vertical contracting between manufacturers and retailers in the supermarket industry. I estimate demand and use the estimates to compute price-cost margins for retailers and manufacturers under different supply models without observing wholesale prices. I then test which set of margins seems to be compatible with the margins obtained from direct estimates of cost and select the best among the non-nested competing models. The models considered are: (1) a double marginalization pricing model; (2) a vertically integrated model; and (3) a variety of alternative (strategic) supply scenarios, allowing for collusion, non-linear pricing and strategic behavior with respect to private label products. Using data on yogurt sold at several stores in a large urban area of the United States, I find that wholesale prices are close to marginal cost and that retailers have pricing power in the vertical chain. This is consistent with non-linear pricing by the manufacturers or with high bargaining power of the retailers.

This resource paper is intended to give a historical account of the development of the methodology of disaggregate behavioral travel demand analysis and its connection to random utility maximization (RUM). It reviews the early development of the subject, and major methodological innovations over the past three decades in choice theory, data collection, and statistical tools. It concludes by identifying some topics and issues that deserve more work, and fearlessly forecasting the future course of research in the field.

: We compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference data elicited households' preferences among gas, electric, methanol, and CNG vehicles with various attributes. The mixed logit models provide a much better fit to these data, and forecasting exercises demonstrate substantial differences between logit and mixed logit model forecasts. ACKNOWLEDGEMENTS: Tom Golob helped collect the data and conducted preliminary analyses upon which our analysis relies. We are grateful to him for allowing us to use these data. Hongyan Sheng provided invaluable assistance with preparing the data for analysis. Collection of the data used in this paper was financed by Southern California Edison Co,. Pacific Gas and Electric Co., and the California Energy Commission. Of course, none of these people or agencies are responsible for any errors or representations that we make in this paper. 1 1. INTRODUCT...

Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation problems to better handle the case where complex data distributions arise from a mixture of simpler underlying (latent) distributions. We develop a theoretical framework for characterizing data as a mixture of maximum entropy models. We formulate a maximum-likelihood interpretation of the mixture model learning, and derive a generalized EM algorithm to solve the corresponding optimization problem. We present empirical results for a number of data sets showing that modeling the data as a mixture of latent maximum entropy models gives signi cant improvement over the standard, single component, maximum entropy approach.

The area of travel demand analysis has in recent years been greatly enriched by the development of new model forms that can accommodate complex patterns of substitution and taste variation. However, this added flexibility comes at a cost of greater complexity in estimation, to the degree that these models need to be estimated through simulation. While basic Monte-Carlo integration can lead to acceptable results, the cost of the simulation process can be decreased significantly by using quasi-Monte-Carlo integration, where the simulation process is based on quasi-random number draws rather than pseudo-random number draws. A popular type of quasi-random sequence in this context is the Halton sequence, in its different forms. In this paper, we compare the performance of standard, scrambled and shuffled Halton sequences in the estimation of various Mixed Logit models. The analysis shows that, while the scrambled Halton sequence offers some improvements over the standard Halton sequence, it is generally outperformed by the shuffled Halton sequence. The fact that the shuffled Halton sequence has further advantages in terms of implementation and generalisation makes it an appealing alternative to the scrambled Halton sequence in the simulationbased estimation of high-dimensional models.