This course aims to provide students with a solid understanding of the modern empirical tools used in empirical industrial economics (EIE) to analyse consumer behaviour, (strategic) behaviuor of firms and market outcomes. EIE combines formal economic theory, knowledge of relevant institutions, micro-economic data, sophisticated econometric tools and computer programming to evaluate how markets function and how much market power firms have.
The course covers estimation of static demand (incl. elementary discrete choice models), estimation of marginal costs and mark-ups of firms, measurement of productivity, as well as basics of simulation of counterfactual outcomes. By doing so the course helps the students to develop skills needed to conduct solid analysis in EIE. The methods covered are also widely used in marketing, health economics and international trade. Students learn to implement some of the estimation techniques covered in the course using econometric software Stata.
Doctoral students can also take this course.
You have the capacity to understand complicated econometric analyses of market outcomes and can analyse markets and competition using microeconomic data.
|After completing the course, you will be able to
- study consumers’ preferences (e.g., price-elasticity, preference for different product characteristics)
- evaluate the degree of competition in a market and estimate mark-up of firms
- conduct simple counterfactual simulations of policies
- evaluate productivity of firms
|International Learning Experience
We will study international market using international data sets.
You should be familiar with basic concepts in microeconomics, industrial organization and econometrics. The participants should have taken a course in intermediate microeconomics (such as 26032 Mikroekonomisk företags- och samhällsanalys, or equivalent), and an intermediate level course on econometrics (such as 3606 Econometrics, or equivalent). You should have some experience in using econometric software, such as Stata. Knowledge of other programming languages, such R, Matlab or
Python, is also useful.
|Total Student Workload
134 hours divided into
Scheduled (contact) hours: 30 h (24h lectures + 6h tutorial & problem sets discussion)
Non-scheduled work: 104 h
Lectures + exercises + project assignment. Kindly note that you are expected to be present at the first lecture session to confirm your participation in the course.
|Literature and Course Material
There is no textbook available, but the course material is partially and selectively based on the following handbook chapter and articles:
- Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. The RAND Journal of Economics, 25(2), 242-262.
- Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 31(3), 395-421.
- Reiss, P. C. & Wolak, F. A. (2007). Structural Econometric Modeling: Rationales and Examples from Industrial Organization, in: Heckman, J. J. & Leamer, E. E. (eds.) (2007), Handbook of Econometrics, edition 1, volume 6A, chapter 64, Elsevier. Chapters 4, 6 and 7.
Project assignement (50%)
Active lecture participation (20%)
|Non-degree studies (Open University, JOO and Contract Studies)
Open university quota: 3
Quota for JOO-students: 3