FIE401 Financial Econometrics

• Topics

  This course introduces students to the main econometric methods and techniques. The course focuses on practical applications of econometrics to financial data using R (free programming language). The mathematics of econometrics is introduced only as needed and is not a central focus. No prior knowledge of econometrics is required.

  Topics covered:

  • Introduction to R
  • Elements of statistics
  • Simple and multiple regression models
    • Possible application: CAPM and Fama-French three factor asset pricing models
  • Regression with a binary dependent variable
    • Possible application: Determinants of the choice of the mode of payment in M&As
  • Regression with panel data
    • Possible application: Capital structure regressions
  • Instrumental variables regression
    • Possible application: CEO succession decision in family firms
  • Quasi experiments
    • Possible application: Evaluation of macro-prudential policies such as loan-to-value cap for housing loans
  • Presentation of econometric analysis
    • Possible application: Master thesis or any report presenting econometric analysis

• Learning outcome

  Knowledge

  The candidate

  • Understands what assumptions econometric models are based on.
  • Knows the econometric methods necessary for doing empirical analysis in finance.
  • Understands the relationships between the theoretical concepts taught in finance class and their application in empirical studies.

  Skills

  The candidate can

  • Conduct, interpret and critically deal with empirical studies in finance and related fields.
  • Identify the advantages and disadvantages of the various methods and techniques.
  • Use R for doing econometric analysis.

  General competence

  The candidate

  • Has the tools and knowledge necessary to define, design and deliver an academically rigorous piece of research.

• Teaching

  The course consists of a combination of pre-recorded lectures and lab sessions where students learn to use R for financial data analysis. In particular, every week the course offers:

  • A pre-recorded Video lecture: The student has to watch the video by him/herself. After being published, the video lectures will be available for the remaining time of the semester. 
  • A 3-hour lecture on-campus which consists of: 
    • 1 hour Q&A based on the Video lecture.
    • 2 hours of lab session implementing econometric analysis in R.

  Pdf solutions of the lab session exercises will be published online after the lab session.

• Restricted access

  None.  

• Recommended prerequisites

  None.

• Required prerequisites

  None.

• Credit reduction due to overlap

  This course was previously taught as FIE449 and FIE401A/B and cannot be combined with any of these courses.

  The course cannot be combined with BUS444 Økonometri for regnskap og økonomisk styring, BUS444E Econometrics for Business Research, BAN431 Econometrics and Statistical Programming, ECN402 Econometrics.

• Compulsory Activity

  Three group assignments. Each group should have three to four members and hand in one solution per group. Assignments must be written in English and must be submitted in the same semester.

  Groups of other size might be allowed upon receiving permission from the course responsible.

  Grading scale: Approved / Not Approved

• Assessment

  The final grade has two components:

  1.    A three-day digital take-home exam in groups of three to four people (60%). Grades can be appealed. The three day take-home exam is held between 09:00 at the first day of examination and 14:00 on the third day of examination.

  2.    Subsequent presentation in the same groups, including a question and answer session based on the topics covered during the course (40%). Grades are individual. Grades cannot be appealed.

  Groups of other size might be allowed upon receiving permission from the course responsible.

  The course is taught in English, hence the take-home exam as well as the subsequent presentation must be in English. In case a students wants to re-take the exam, both the oral and the written part have to be re-taken. 

• Grading Scale

  A-F

• Computer tools

  Participants should bring their laptops to  all sessions. All applications covered in the course will be implemented in RStudio (an open-source software for R programming language). Download and installation instructions will be provided.

• Literature

  Stock and Watson, Introduction to Econometrics, Global Edition, 4th edition

  Florian Heiss, Using R for Introductory Econometrics