FIE401 Financial Econometrics
Spring 2023Autumn 2023
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.
- 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
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.
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 session 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.
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.
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
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 presentation in the same groups including a question and answer session (based on the topics covered during the course). 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.
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.
Stock and Watson, Introduction to Econometrics, Global Edition, 4th edition
Florian Heiss, Using R for Introductory Econometrics
- ECTS Credits
- Teaching language
Autumn and Spring. Offered Spring 2023.
Part of studies
Spring: Assistant Professor Darya Yuferova, Department of Finance, NHH.
Autumn: Assistant Professor Maximilian Rohrer, Department of Finance, NHH.