Description:
Evaluating marketing decisions and developing goal-oriented marketing strategies, e.g. maximizing firm profits, depend on the measurement of causal relationships between firms' objectives and marketing activities. In this course, we discuss in depth advanced methods to empirically determine the causal relationship between marketing activities and firms' objectives. In exercise courses students learn how to apply these methods to real data. Special attention is given to modeling the effects of marketing on sales and market share data. In this course we also focus on discrete choice models for individual purchase data and aggregate sales data. Successful participation in this class will enable students to quantify the impact of marketing on key performance measures and to evaluate the success of marketing activities.
Literature:
Berry, S.T. (1994), Estimating Discrete-Choice Models of Product Differentiation, RAND Journal of Economics, Vol. 25 (2), 242-262.
Berry, S. T., Levinsohn, J. & Pakes, A. (1995), Automobile prices in market equilibrium, Econometrica 63(4), 841-890.
Chintagunta, P., V. Kadiyali and N. Vilcassim (2004), Structural Models of Competition: A Marketing Strategy Perspective, Assessing Marketing Strategy Performance, eds. C. Moorman and D. Lehmann, Cambridge: Marketing Science Institute, 95-113.
Conlon, Christopher, and Jeff Gortmaker (2020). Best practices for differentiated products demand estimation with PyBLP. RAND Journal of Economics, 51 (4), 1108-1161.
Nevo, A. (2000), A Practitioner’s Guide to Estimation of Random-Coefficient Logit Models of Demand, in: Journal of Economics & Management Strategy, Vol. 9(4), 513-548.
Train, K.E. (2009), Discrete Choice Methods with Simulation, Cambridge University Press, Chapter 3, 4, 6, 8, 9, 10.
Lecturers:
Daniel Klapper
Time & venue:
Wednesdays, 12:00-14:00; HU Berlin, Spandauer Str. 1, room 22
Thursdays, 12:00-14:00; HU Berlin, Spandauer Str. 1, room 22
Exam:
Portfolio exam: 4 assignments
More information can be found on Moodle.
