The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

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.

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Discrete Choice Methods with Simulation

People like to help those who are helping them and to hurt those who are hurting them. Outcomes rejecting such motivations are called fairness equilibria. Outcomes are mutual-max when each person maximizes the other's material payoffs, and mutual-min when each person minimizes the other's payoffs. It is shown that every mutual-max or mutual-min Nash equilibrium is a fairness equilibrium. If payoffs are small, fairness equilibria are roughly the set of mutual-max and mutual-min outcomes; if payoffs are large, fairness equilibria are roughly the set of Nash equilibria. Several economic examples are considered and possible welfare implications of fairness are explored. Copyright 1993 by American Economic Association.

https://www.jstor.org/stable/pdfplus/2117561.pdf

Incorporating Fairness into Game Theory and Economics

This paper develops techniques for empirically analyzing demand and supply in differentiated products markets and then applies these techniques to analyze equilibrium in the U.S. automobile industry. Our primary goal is to present a framework which enables one to obtain estimates of demand and cost parameters for a class of oligopolistic differentiated products markets. These estimates can be obtained using only widely available product-level and aggregate consumer-level data, and they are consistent with a structural model of equilibrium in an oligopolistic industry. When we apply the tech- niques developed here to the U.S. automobile market, we obtain cost and demand parameters for (essentially) all models marketed over a twenty year period.

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Automobile prices in market equilibrium

SUMMARY Thispaperconsidersmixed,orrandomcoeAcients,multinomiallogit (MMNL)modelsfordiscreteresponse, andestablishesthefollowingresults.Undermildregularityconditions,anydiscretechoicemodelderivedfrom random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNLmodel.PracticalestimationofaparametricmixingfamilycanbecarriedoutbyMaximumSimulated LikelihoodEstimationorMethodofSimulatedMoments,andeasilycomputedinstrumentsareprovidedthat make the latter procedure fairly eAcient. The adequacy of a mixing specification can be tested simply as an omittedvariabletestwithappropriatelydefinedartificialvariables.Anapplicationtoaproblemofdemandfor alternativevehiclesshowsthatMMNL provides aflexible and computationally practical approach todiscrete response analysis. Copyright # 2000 John Wiley & Sons, Ltd.

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Mixed mnl models for discrete response

Mixed logit models, also called random-parameters or error-components logit, are a generalization of standard logit that do not exhibit the restrictive “independence from irrelevant alternatives” property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households' choices of appliances under utility-sponsored programs that offer rebates or loans on high-efficiency appliances.

Mixed logit with repeated choices: households' choices of appliance efficiency level

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. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

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.

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Kenneth Train

Papers

Discrete Choice Methods with Simulation

Mixed mnl models for discrete response

Mixed logit with repeated choices: households' choices of appliance efficiency level

Discrete Choice Methods with Simulation

Recreation Demand Models with Taste Differences Over People