Time Series Methods in Financial Econometrics Econ 509 B1 - Winter 2020
Instructor: Sebastian Fossati Office: Tory 7-11
Email: [email protected]
Website: http://www.ualberta.ca/~sfossati/
Office Hours: TBD (see course website)
Lecture
Monday and Wednesday 11:00-12:20 in T 1 83 (Tory Building).
Course Objectives
This course will cover topics in time series econometrics with focus on economic forecasting and applications in macroeconomics and finance. The topics we will cover include: dynamic regres- sion; forecast evaluation; univariate time series models; non-stationarity; multivariate time series models; co-integration and error-correction models; mixed frequency data; volatility models.
Course Prerequisites
I will assume everyone has a good understanding of basic mathematical statistics, linear algebra, linear algebra based econometrics, and maximum likelihood. Previous knowledge of time series econometrics is not assumed.
Grading
The final grade will be based on four homework assignments (10%), a short term paper (30%), a midterm exam (30%), and a final exam (30%). Further details will be provided as assignments are distributed. Regular class participation is expected. For this course, no extra credits are available.
Grades reflect judgments of student achievement made by instructors. These judgments are based on a combination of absolute achievement and relative performance in a class.
Special notes:
- Homework assignments will be a combination of computer problems using R and analytical problems. Everyone must turn in their own assignments, but collaboration is permitted.
Collaboration on the computer problems is encouraged. Late homework assignments will not be accepted. Solutions will follow after the assignments are handed in.
- The term paper will be a short length (under 20 pages) research paper. This report (and the associated proposal) will have cumulative late penalties. The report must be completed
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in order to receive credit for this course. Detailed instructions will be distributed later.
Proposal due date: Wednesday February 26 at 11:59 am.
Final report due date: Wednesday April 15 at 11:59 am.
- Midterm Exam: Wednesday February 12 at 11:00 am (in class).
- Final Exam: Wednesday April 22 at 2:00 pm (school schedule).
Textbook
Ghysels and Marcellino (2018) will be our main reference. Enders (2015) offers an accessible introduction to time series econometrics with numerous real-world examples.
- Ghysels, E. and Marcellino, M. (2018): Applied Economic Forecasting using Time Series Methods.
Additional Textbooks
You may also find the following textbooks useful.
- Enders, W. (2015): Applied Econometric Time Series.
- Hamilton, J.D. (1994): Time Series Analysis.
- Hyndman, R., and Athanasopoulos, G. (2018): Forecasting: Principles and Practice.
- Elliott, G. and Timmermann, A. (2016): Economic Forecasting.
- Tsay, R.S. (2010): Analysis of Financial Time Series.
Computing
R is used extensively in the course. R is a free software environment for statistical computing and graphics (http://www.r-project.org). The course website has links to some R manuals for beginners and the Use R! series of books can be downloaded for free from the university library. These books will help you implement the techniques covered in class.
- Zuur, A.F., Ieno, E.N., and Meesters, E. (2009), A Beginner’s Guide to R.
- Cowpertwait, P. and Metcalfe, A. (2009), Introductory Time Series with R.
- Pfaff, B. (2008), Analysis of Integrated and Cointegrated Time Series with R.
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Course Outline
Note: GM denotes “Ghysels and Marcellino”, E denotes “Enders”.
1. Introduction to time series 2. Introduction to R
3. The dynamic regression model - GM ch. 3
4. Forecast evaluation
- GM ch. 4; E ch. 2 (2.9, 2.10, 2.13) 5. Univariate time series models
- GM ch. 5; E ch. 2, 4 6. VAR models
- GM ch. 6; E, ch. 5 7. Error correction models - GM ch. 7; E ch. 6 8. Modeling volatility
- GM ch. 14; E ch. 3 9. State-space models
- GM ch. 11
10. Models for mixed frequency data - GM ch. 12
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Missed / Deferred Exams
There will be no make-up midterm exam. A student who misses the midterm exam because of incapacitating illness, severe domestic affliction or other compelling reason (including religious conviction) may have the percentage weight transferred to other class work.
More information can be found in §23.3 of the University Calendar.
Accessibility Resources (formerly Student Accessibility Services)
If you have a condition that may require some classroom or exam modifications, please contact Accessibility Resources to obtain a determination as to what accommodations should be made.
Academic Success Centre
The Academic Success Centre offers a variety of learning resources, including a variety of work- shops in learning effective study and exam strategies.
Centre for Writers
The Centre for Writers offers free one-on-one writing coaching to all students. Students can request consultation for a writing project at any stage of development.
Notes
Policy about course outlines can be found in Course Requirements, Evaluation Procedures and Grading in the University Calendar.
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at www.governance.ualberta.ca) and avoid any behaviour that could poten- tially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence.
Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
Audio or video recording, digital or otherwise, of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as part of an approved accommo- dation plan. Student or instructor content, digital or otherwise, created and/or used within the context of the course is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the content author(s).
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