Advanced Business Econometrics

MKT 903

Lecturer Prof. Dr. Florian Stahl
Course Format Lecture
Credit Points 6 ECTS
Hours per Week 4
Semester Fall
Language English
Registration Please register for the course at the Center for Doctoral Studies in Business (CDSB)
Accepted Participants CDSB PhD Students, CDSE PhD Students, Mannheim Master in Business Research (MMBR)

Further Information

  • Brief Description

    The goal of the course is to provide Ph.D. students an introduction in and overview of state-of-the-art discrete choice methods in business and marketing research. 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, method of simulated moments, and method of simulated scores. The course will also cover procedures for endogeneity and expectation-maximization algorithms. Participants will study a variety of articles and case studies which demonstrate the application of such models to real business phenomena.

    The lectures on “Advanced Business Econometrics” cover the following topics:

    • Properties of Discrete Choice Model 
    • Logit Model
    • Numerical Maximization 
    • Nested Logit 
    • Probit Model
    • Mixed Logit
    • Conditional Distributions of Individual-level Parameters
    • Endogeneity: BLP, Control functions, Latent Instruments
  • Lecture

    Lecturer Prof. Dr. Florian Stahl
    Schedule Please refer to the latest information on Portal2 and ILIAS
    Assessment Homework, Written Exam
  • Required Readings

    • Ben-Akiva M. and D. Bolduc (1996): „Multinomial Probit with a Logit Kernel and a General Parametric Specification of the Covariance Structure,“ working paper. 
    • Berry, S. (1994): „Estimating Discrete Choice Models of Product Differentiation,“ The Rand Journal of Economics, Vol. 25, No. 2, pp. 242–262.
    • Berry, S., A. Pakes, and J. Levinsohn (1995): „Automobile Prices in Equilibrium,“ Econometrica,Vol. 63, No. 4, pp. 841–890.
    • Berry, S., A. Pakes, and J. Levinsohn (2004): „Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Vehicle Market,“ Journal of Political Economy, Vol. 112, No. 1, pp. 68–105.
    • Brownstone D. and K. Train (1998/99): „Forecasting New Product Penetration with Flexible Substitution Patterns,“ Journal of Econometrics, Vol. 89, No. 1–2, pp. 109–129
    • Hausman,  J. and D. Wise, „A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogenous Preferences,“ Econometrica, Vol. 48, No. 2, pp. 403–426.
    • Lerman S. and C. Manski (1981): „On the Use of Simulated Frequencies to Approximate Choice Probabilities,“ in C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications, Cambridge, MA, MIT Press, 1981.
    • McFadden,  D. (1978): „Modeling the Choice of Residential Location,“ [PDF] in A. Karlquist, et al. (eds.), Spatial Interaction Theory and Planning Models, Amsterdam, North-Holland Publishing Company.
    • McFadden,  D. (1994): „Conditional Logit Analysis of Qualitative Choice Behavior,“ [PDF] in P. Zarembka (ed.), Frontiers of Econometrics, New York, NY, Academic Press.
    • McFadden D. and K. Train (2000): „Mixed MNL Models of Discrete Response,„Journal of Applied Econometrics, Vol. 15, No. 5, pp. 447–470
    • Park S. and S. Gupta (2009): „A Simulated Maximum Likelihood Estimator for the Random Coefficient Logit Model Using Aggregate Data,“ Journal of Marketing Research,Vol. 46, No. 4, pp. 531–42. 
    • Petrin A. and K. Train (2010): „A Control Function Approach to Endogeneity in Consumer Choice Models,“ Journal of Marketing Research,Vol. 47, No. 1, pp. 3–13.
    • Revelt  D. and K. Train (1998): „Mixed Logit with Repeated Choices,“ Review of Economics and Statistics, Vol. LXXX, No. 4, pp. 647–657.
    • Ruud, P. (2000): An Introduction to Classical Econometric Theory, New York, Oxford University Press.
    • Staelin, Richard (1994): How to Write Readable Papers for Marketing Science
    • Train,  K. (1978): „A Validation Test of a Disaggregate Mode Choice Model,“ Transportation Research, Vol. 12, pp. 167–174. 
    • Train,  K. (1986): „Qualitative Choice Analysis“, Cambridge, MA, MIT Press.
    • Train, K., D. McFadden, and M. Ben-Akiva (1987): „The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices,“ RAND Journal of Economics, Vol. 18, No. 1, pp. 109–123.
    • Train  K. and M. Weeks (2005): „Discrete Choice Models in Preference Space and Willingness-to-pay Space,“ in Applications of Simulation Methods in Environmental and Resource Economics, R. Scarpa and A Alberini, eds., Springer, Dordrecht.
    • Train K. and C. Winston (2007): „Vehicle Choice Behavior and the Declining Market of US Automakers,“ Internatinal Economic Review, Vol. 48, No. 4, pp. 1469-1496.
    • Train, K. E. (2009): „Discrete Choice Methods with Simulation“, Cambridge, Second Edition. The book can be downloaded from
    • Walker, J., M. Ben-Akiva, and D. Bolduc (2007): „Identification of Parameters in Normal Error Component Logit Mixture (NECLM) Models,“ Journal of Applied Econometrics, Vol. 22, pp 1095-1025. 
  • Required Software

    The seminar will include practical exercises; participants should bring a laptop and should download and install R from  before the course.