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.

  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;
  • is able to use R for doing econometric analysis.

  SKILLS - The candidate...

  • will be able to conduct, interpret and critically deal with empirical studies in finance and related fields;
  • will be able to identify the advantages and disadvantages of the various methods and techniques;
  • will be able to understand the relationships between the theoretical concepts taught in finance class and their application in empirical studies;

  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 discussion of the Video lecture
    • 2 hours of lab session implementing econometric analysis in R

  Due to Covid-19 and the related restrictions, the 3-hour lecture will be streamed online. Real-time interaction between students participating remotely and the lecturer through means of chat, etc. will be possible and encouraged. However, the videos for the 3-hour lecture will be taken offline after two days. 

• Restricted access

  None.  

• Recommended prerequisites

  None.

• Required prerequisites

  None.

• Credit reduction due to overlap

  This course was taught before 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 Econometric Techniques.

• Requirements for course approval

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

  Grading scale: Approved / Not Approved

• Assessment

  The final grade has two components:

  1.    A three-day digital take-home exam in groups of three-four people. Grades can be repealed. (60%)

  2.    Subsequent digital presentation in groups of three-four people including a question and answer session. Grades are individual. Grades cannot be appealed. (40%)

  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. 

  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.

• 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