Workshop on Discrete Choice Modeling

A course for the graduate program at CES.
Taught by Dr. Kurt Schmidheiny (Tufts University)
September 2005

Course Outline:

This course introduces discrete choice modeling with cross-section data. Discrete choice models are used to analyze individual choice behavior of consumers, households, firms, and other agents. Applications of these models include transportation mode, brand choice, recreation demand, telecommunications services, health services, location decisions and a wide variety of other settings in many diverse fields. The course focuses on the estimation of models that are based on random utility maximization (RUM) and covers the basic models (conditional and nested logit) as well as recently developed simulation based models (multinomial probit and mixed logit). The course is organized as a workshop to give students hands-on experience in the application of the econometric techniques.

Resources (files are password protected):

Applications:

• Data and Stata files
• A Quick Guide to Stata 8 for Windows

Lecture Notes:

• Multinomial Choice (Basic Models)
• Slides: Type 1 Extreme Value
• Slides: The Nested Logit Model
• Slides: The Multinomial Probit Model
• Slides: The Mixed Logit Model

The methods in a textbook:

• Train, Kenneth E. (2003), Discrete Choice Methods with Simulation, Cambridge University Press. Chapters 1-6.
• Moshe Ben-Akiva and Steven Lerman (1985), Discrete Choice Analysis Theory and Application to Travel Demand, MIT Press.

The methods in other introductory readings:

• Greene, William H. (2003), Econometric Analysis, Prentice Hall. Sections 21.7-21.8.
• Wooldridge, Jeffrey M., Econometric Analysis of Cross Section and Panel Data, MIT Press. Sections 15.9 - 15.10.
• Amemiya, Takeshi (1994), Introduction to Statistics and Econometrics, Harvard University Press. Sections 13.5.
• Amemiya, Takeshi (1985), Advanced Econometrics, Harvard University Press. Chapter 9.3.

Further (easy) readings:

1. Ben-Akiva, M., D. McFadden, T. Garling, D. Gopinath, J. Walker, D. Bolduc, A. Boersch-Supan, P. Delquie, O. Larichev, T. Morikawa, A. Polydoropoulou and V. Rao (1999), Extended Framework for Modeling Choice Behavior, Marketing Letters, 10(3), 187-203.
2. Ben-Akiva, McFadden, Train, Walker, Bhat, Bierlaire, Bolduc, Boersch-Supan, Brownstone, Bunch, Daly, de Palma, Gopinath, Karlstrom, and Munizaga (2002), Hybrid Choice Models: Progress and Challenges, Marketing Letters, 13(3), 163-175.
3. Heiss, Florian (2002), Structural Choice Analysis with Nested Logit Models, The Stata Journal, 2(3), 227–252.
4. Hensher David A. and William H. Greene (2002), Specification and Estimation of the Nested Logit Model: Alternative Normalisations. Transportation Research Part B: Methodological, 36(1), 1-17.
5. Hensher, David A. and William H. Greene (2003), The Mixed Logit Model: The State of Practice, Transportation, 30(2), 133-176.
6. McFadden, Daniel (2001), Economic Choices, American Economic Review, 91, 351-378. Nobel Price Lecture.
7. Walker, Joan (2002), The Mixed Logit (or Kernel Logit) Model: Dispelling Misconceptions of Identification, Transportation Research Record, 1805, 86-98.

The methods in (advanced) articles:

1. Hausman and D. Wise (1978), A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogenous Preferences, Econometrica, 46/2, 403-426.
2. McFadden, Daniel (1974), Conditional Logit Analysis of Qualitative Choice Behavior, in P. Zarembka (ed.), Frontiers of Econometrics, New York, NY, Academic Press, 1974.
3. McFadden, Daniel (1978), Modeling the Choice of Residential Location, in A. Karlquist et al. (eds.), Spatial Interaction Theory and Planning Models, Amsterdam, North-Holland Publishing Company, 1978.
4. McFadden, Daniel and Kenneth Train (2000), Mixed MNL Models of Discrete Response, Journal of Applied Econometrics, 15/5, 447-470.