FIN544 Econometric programming in finance

• Topics

  The course focuses on econometric programming with Stata and Python, with applications in asset pricing and corporate finance. After introducing students to advanced programming techniques using Stata, including Python integration and LM studio usage, applications to real datasets will be implemented. These will go from classic financial econometric tests (e.g.: Fama-McBeth) to modern approaches of measuring causal relations (instrumental variable based approaches, regression discontinuity design and difference-in-difference tests) and paper replications (e.g.: Hombert, J. and Matray, A. (2018), Can Innovation Help U.S. Manufacturing Firms Escape Import Competition from China?, Journal of Finance, 73(5), 2003-2039). Model selection using machine learning based approaches (LASSO and Elastic Nets in particular) and natural language processing techniques will also be explored.

• Learning outcome

  After completing the course, students will be able to:

  Knowledge

  • Identify the assumptions underlying causal‑relation estimation techniques in empirical finance.
  • Organize research projects that use Stata and Python to combine classic econometric tools with modern machine‑learning approaches.

  Skills

  • Use Stata and Python to implement modern econometric techniques, including querying machine‑learning‑based online services via Python APIs.
  • Implement natural language processing techniques using local large language models (LLMs).
  • Select appropriate identification strategies to establish causal relations.

  General competenc

  • Conduct independent empirical finance research that integrates advanced econometric programming, causal identification strategies, and modern machine‑learning and NLP methods.

• Teaching

  The teaching will take the form of lectures on campus, organized during three weeks of 4 days (4 hours a day, in the morning).

• Restricted access

  • PhD candidates at NHH.
  • PhD candidates at Norwegian institutions.

• Required prerequisites

  •Successful completion of PhD-level corporate finance and asset pricing courses;

  •Successful completion of PhD-level course in financial econometrics;

• Compulsory Activity

  Students are required to be present in all the classes. 

  Compulsory activities (work requirements) are valid for one semester after the semester they were obtained.

• Assessment

  Participation in lectures (50%)

  Students will be assessed individually on their in-class applications of econometric programming techniques to real-world datasets, with an emphasis of coding using a combination of Python and Stata, supported by the use of an LLM.

  Individual research project (50%)

  Students will develop a research project applying the tools and techniques presented during the course. The project must result in a written report, submitted at the end of the term.

  Both parts must be passed in the same semester.

• Grading Scale

  A - F

• Computer tools

  The students are expected to have a computer and a working license of Stata 19.

• Literature

  Christopher F. Baum, An Introduction to Stata Programming, Second Ed., Stata Press

  A. Colin Cameron and Prawin K. Trivedi, Microeconometrics Using Stata, Second Ed. Stata Press