Data Driven Business Analysis

ECN431 Data Driven Business Analysis

Autumn 2022

Spring 2023
  • Topics

    As an analyst, manager or economist, there are many important problems and questions you can only address by analyzing 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?"

    Quantitative 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 empirical methods to estimate relevant unknown quantities.

    In this course, you will learn about the main features of several central markets in the economy, while being introduced to methods and models that will allow you to analyze important problems 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

    After completing the course, you will:


    • have sound knowledge of economic models of market behavior and imperfect competition
    • understand the relationship between economic models, data, and econometric analysis
    • be familiar with the competitive environment and market structure in several central industries


    • choose the relevant economic model 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

    Lectures and computer labs. Lectures will be streamed and recorded.

  • 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.

  • Compulsory Activity

    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 in the lab. It will also be possible to use STATA, Python or Julia to complete the assignments.

  • Literature


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

    Selected academic articles and chapters from:

    • Aguirregabiria, V. (2019). 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).


ECTS Credits
Teaching language

Spring. Offered Spring 2022.

Course responsible

Associate Professor Morten Sæthre, Department of Economics

Assistant Professor Mateusz Mysliwski, Department of Economics (main course responsible)