Postgraduate Course: Advanced Time Series Econometrics (ECNM11049)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This is a course in advanced time series econometrics with a focus on the models used in macroeconomics and finance. It will cover four main classes of models: state space models, nonlinear time series models, factor models, and models for use with mixed frequency data. The focus of the course will be on using these models in practice. To this end, the lectures will discuss the models and their properties and show how to estimate and forecast with them using R. In addition to 14 hours of lectures, the course will include four hours of computer labs where students will gain experience in working with these models. The course will be assessed through a final exam. |
| Course description |
This course takes place in block 4 (semester 2) over six weeks and involves lectures and computer sessions. The lectures are given by Niko Hauzenberger, Gary Koop and Ping Wu and the computer sessions are given by Ping Wu.
You can find out more about the teaching team from their websites:
https://sites.google.com/site/garykoop/
https://nhauzenb.github.io/
https://pingwu.org/
The course will cover four main sets of models:
State space models: Unobserved components and trends
Non-linear time series models: Structural breaks, Markov switching and threshold models, stochastic volatility
Factor models for Big Data: The static and dynamic factor models
Mixed frequency models: mixed data sampling (MIDAS), the stacked VAR and the state space VAR
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Econometrics 1 (ECNM11043) AND (
Econometrics 2 - Time Series (ECNM11089) OR
Econometrics 2 - Microeconometrics (ECNM11091))
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students may take either of the Econometrics 2 courses as a pre-requisite for this course, but are strongly advised to take Econometrics 2 - Time Series (ECNM11089).
Students should be enrolled on MSc Economics, MSc Economics (Econometrics), MSc Economics (Finance) or MSc Mathematical Economics and Econometrics.
Any other students must email sgpe@ed.ac.uk in advance to request permission.
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Information for Visiting Students
| Pre-requisites | Students should be enrolled on MSc Economics, MSc Economics (Econometrics), MSc Economics (Finance) or MSc Mathematical Economics and Econometrics.
Any other students must email sgpe@ed.ac.uk in advance to request permission.
|
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 14,
Supervised Practical/Workshop/Studio Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Assessment will be through a final 2-hour exam in the April/May Diet worth 100% of the grade.
|
| Feedback |
In the week following the publication of the course results, students will be able to submit requests to obtain a copy of their script. The course organiser may arrange to meet with the students, either in-person or online, to discuss their exam performance. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- This module explores further topics in time series econometrics, beyond Econometrics 2. Students will be introduced to various tools that are part of the basic econometric training of professional economists. The course is intended for students who want to be professional economists or who want to go on to PhD study. It also is very relevant to those planning to work or research in finance and/or macroeconomics.
- The learning outcomes are partially assessed through the formal assessment on the course. Students will have an opportunity to attain these outcomes through their engagement with the course such as attendance of lectures and participation in computer sessions, and also through independent study of the material.
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Reading List
The primary reading for the course is:
Ghysels, E. and Marcellino, M. (2018) Applied Economic Forecasting Using Time Series Methods.
Another good textbook which covers much of the course material is:
Tsay, R. (2010) Analysis of Financial Time Series (third edition). |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | |
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