BAN440 Data Driven Business Analysis (replacing ECN431)

• Topics

  As an analyst, manager or economist, there are many important problems and questions you can only address by analysing data. For instance, "if we increase the price of one product by 5%, what is the likely effect on demand for this and other products in our portfolio, as well as competitors' products?", "what are the consequences of high concentration in grocery retailing?", or "how does regulation change firm and consumer behavior?"

  Empirical analysis of problems in business strategy, competition policy and market regulation requires both knowledge of the specific market, appropriate models to account for important mechanisms and structure the analysis, and ability to choose and correctly use quantitative methods to estimate unknown quantities.

  In this course, you will analyse data from important markets in the economy, while being introduced to methods and models that will allow you to answer key questions related to competition, consumer behavior, regulation and evolution of business. You will be equipped with sufficient knowledge to interpret empirical results and convey the information in professional settings.

  We plan to cover the following markets, with accompanying models and empirical methods:

  • Electricity markets
    • ​Competitive supply and demand
    • Instrumental variables and estimating systems of equations
  • Grocery retailing
    • Product differentiation and pricing of product portfolios
    • Discrete choice methods
    • Machine learning for demand prediction
  • Banking and local competition
    • Market structure and sunk costs
    • Entry cost estimation
  • Pharmaceuticals
    • Innovation, intellectual property rights and patents
    • Differences-in-differences and value of patents
  • Production and distribution of beverages
    • Efficiency gains and competition loss in mergers and acquisitions
    • Merger simulation and analysis

• Learning outcome

  Upon completion of the course, the student can:

  Knowledge:

  • interpret economic models of market behaviour and imperfect competition
  • identify the the relationship between economic models, data, and econometric analysis
  • discuss the competitive environment and market structure in several central industries

  Skills:

  • use economic models to answer questions related to market structure, entry, effects of mergers, pricing and technological change
  • apply economic theory and suitable econometric methods to make sense of market- and firm-level data  
  • use statistical software to conduct relevant analyses, produce professional tables and figures, and replicate results at a later time

  General competence:

  • carry out an independent analysis, for instance as part of a master thesis, or in your future professional career
  • present and communicate results of data driven projects in a professional context

• Teaching

  The course consists of lectures and computer labs. 

• Recommended prerequisites

  Econometrics equivalent to ECN402.

  Familiarity with basic calculus will be assumed.

  In some cases, familiarity with the concepts introduced in ECN433 or ECO427 (such as: supply and demand, Bertrand and Cournot oligopoly models, Nash equilibrium, relationship between market structure and market power) will be helpful; however, such knowledge is not assumed, and the important points will be covered in class.

• Credit reduction due to overlap

  Cannot be combined with ECN431.

• Compulsory Activity

  Short oral presentation of term paper in English.

• Assessment

  The final grade will be based on two individual assignments - a shorter (10%) and a longer one (20%), and a group-based (3-4 students) term paper (70%).

  The short assignment will be handed out early February, and the second assignment towards the end of February/beginning of March with two-week deadlines. The topic for the term paper should be chosen by March, and the deadline for the term paper will be in April.

  The assignments and term paper must be written in English.  

• Grading Scale

  Grading scale A-F

• Computer tools

  We will use R/RStudio in the lab and provide examples of code.

  It will also be possible to use other software (eg. STATA, Python or Julia) to complete the assignments. 

• Literature

  Textbook:

  • Peter Davis & Eliana Garcés (2009): Quantitative Techniques for Competition and Antitrust Analysis, Princeton University Press

  Selected academic articles and chapters from:

  • Aguirregabiria, V. (2021). Empirical industrial organization: Models, methods, and applications (freely available online).
  • Tirole, J. (1988). The Theory of Industrial Organization. MIT Press.
  • Train, K. E. (2009). Discrete Choice Methods with Simulation. Cambridge University Press (freely available online).