BAN440 Data Driven Business Analysis

• Topics

  Unlock the Power of Data to Drive Strategic Decisions in Complex Markets

  As an analyst, manager, or economist, your ability to solve critical business challenges often hinges on one thing: data. From predicting how a price increase will impact demand across product lines to assessing the effects of market concentration or regulatory changes, data-driven insights are key to making informed, impactful decisions.

  In this course, you’ll dive deep into the empirical analysis of real-world markets—gaining the tools, models, and methods needed to solve complex questions in business strategy, competition policy, and market regulation.

  You’ll explore questions like:

  • What happens to demand when prices rise across a product portfolio?
  • How does high concentration in grocery retail affect consumers and competition?
  • What are the ripple effects of regulation on firm behavior and market dynamics?

  Through case studies of critical markets, you will:

  • Analyze data from sectors like electricity, banking, retail, and pharmaceuticals.
  • Learn to apply sophisticated models like discrete choice methods, instrumental variables, and systems of equations.
  • Harness the power of machine learning for demand prediction and market analysis.
  • Develop skills to estimate unknown quantities, interpret results, and communicate your findings effectively in professional environments.

  Key topics include:

  • Electricity markets: Supply and demand competition, estimating systems of equations
  • Grocery retailing: Product differentiation, portfolio pricing, and discrete choice methods
  • Banking: Local competition, market structure, and entry cost estimation
  • Pharmaceuticals: Innovation, intellectual property, and the value of patents
  • Mergers and acquisitions: Simulating competition, evaluating efficiency gains and losses

  By the end of this course, you’ll have the expertise to navigate and interpret complex market data, enabling you to influence strategic decisions in industries shaped by competition, regulation, and consumer behavior.

• 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.

  Using other software (eg, STATA, Python or Julia) to complete the assignments is possible. 

• 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).