Discrete Choice Methods with Simulation

Transcription

1 Discrete Choice Methods with Simulation Kenneth E. Train University of California, Berkeley and National Economic Research Associates, Inc. iii

2 To Daniel McFadden and in memory of Kenneth Train, Sr. ii

3 Discrete Choice Methods with Simulation 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 (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, the method of simulated moments, and the method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as antithetics 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 topics, which have risen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing. Professor Kenneth E. Train teaches econometrics, regulation, and industrial organization at the University of California, Berkeley. He also serves as Vice President of National Economic Research Associates (NERA), Inc. in San Francisco, California. The author of Optimal Regulation: The Economic Theory of Natural Monopoly (1991) and Qualitative Choice Analysis (1986), Dr. Train has written more than 50 articles on economic theory and regulation. He chaired the Center for Regulatory Policy at the University of California, Berkeley, from 1993 to 2000 and has testified as an expert witness in regulatory proceedings and court cases. He has received numerous awards for his teaching and research. i

4 published by the press syndicate of the university of cambridge The Pitt Building, Trumpington Street, Cambridge, United Kingdom cambridge university press The Edinburgh Building, Cambridge CB2 2RU, UK 40 West 20th Street, New York, NY , USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia Ruiz de Alarcón 13, Madrid, Spain Dock House, The Waterfront, Cape Town 8001, South Africa C Kenneth E. Train 2003 This book is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2003 Printed in the United Kingdom at the University Press, Cambridge Typeface Times Roman 11/13 pt. System L A TEX2ε [tb] A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication Data Train, Kenneth. Discrete choice methods with simulation / Kenneth E. Train. p. cm. Includes bibliographical references and index. ISBN ISBN (pb.) 1. Decision making Simulation methods. 2. Consumers preferences Simulation methods. I. Title. HD30.23.T dc ISBN hardback ISBN paperback iv

5 Contents 1 Introduction page Motivation Choice Probabilities and Integration Outline of Book Topics Not Covered A Couple of Notes 11 Part I Behavioral Models 2 Properties of Discrete Choice Models Overview The Choice Set Derivation of Choice Probabilities Specific Models Identification of Choice Models Aggregation Forecasting Recalibration of Constants 37 3 Logit Choice Probabilities The Scale Parameter Power and Limitations of Logit Nonlinear Representative Utility Consumer Surplus Derivatives and Elasticities Estimation Goodness of Fit and Hypothesis Testing Case Study: Forecasting for a New Transit System Derivation of Logit Probabilities 78 v

6 vi Contents 4 GEV Introduction Nested Logit Three-Level Nested Logit Overlapping Nests Heteroskedastic Logit The GEV Family 97 5 Probit Choice Probabilities Identification Taste Variation Substitution Patterns and Failure of IIA Panel Data Simulation of the Choice Probabilities Mixed Logit Choice Probabilities Random Coefficients Error Components Substitution Patterns Approximation to Any Random Utility Model Simulation Panel Data Case Study Variations on a Theme Introduction Stated-Preference and Revealed-Preference Data Ranked Data Ordered Responses Contingent Valuation Mixed Models Dynamic Optimization 173 Part II Estimation 8 Numerical Maximization Motivation Notation Algorithms Convergence Criterion Local versus Global Maximum Variance of the Estimates Information Identity 205

7 Contents vii 9 Drawing from Densities Introduction Random Draws Variance Reduction Simulation-Assisted Estimation Motivation Definition of Estimators The Central Limit Theorem Properties of Traditional Estimators Properties of Simulation-Based Estimators Numerical Solution Individual-Level Parameters Introduction Derivation of Conditional Distribution Implications of Estimation of θ Monte Carlo Illustration Average Conditional Distribution Case Study: Choice of Energy Supplier Discussion Bayesian Procedures Introduction Overview of Bayesian Concepts Simulation of the Posterior Mean Drawing from the Posterior Posteriors for the Mean and Variance of a Normal Distribution Hierarchical Bayes for Mixed Logit Case Study: Choice of Energy Supplier Bayesian Procedures for Probit Models 316 Bibliography 319 Index 331

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