Postgraduate Course: Bayesian Econometrics (ECNM11060)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and showing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics and these are discussed in in detail. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions, state space models (including factor models and stochastic volatility models) and nonparametric methods such as regression trees. |
| 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 topics covered in the lectures include:
An Overview of Bayesian Econometrics
Bayesian Inference in the Normal Linear Regression Model
Overview of Recent Advances in Bayesian Macroeconometrics
Introduction to Bayesian Machine Learning Methods
Introduction to Bayesian Nonparametrics
Bayesian Vector Autoregressions (VARs)
Bayesian State Space Models
TVP-VARs with Stochastic Volatility
Bayesian Inference in Factor Models
Mixed Frequency Methods for Macroeconomics
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | 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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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 14,
Seminar/Tutorial Hours 4,
Formative Assessment Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Additional Information (Learning and Teaching) |
Project work 30 hours
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment will be through an empirical project and a journal article summary. The empirical project will be worth 60% of the final grade and the journal article summary worth 40%. There will be no exam. |
| Feedback |
Detailed written feedback and marks will be returned within two weeks of assessment submission. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a knowledge and understanding of the basics of Bayesian analysis, VARs, state space modelling and an ability to apply these methods to analyse macroeconomic models; grasp of appropriate computational techniques.
- Demonstrate and develop research and investigative skills such as problem framing and solving and the ability to assemble and evaluate complex evidence and arguments.
- Develop communication skills in order to critique, create and communicate understanding and to collaborate with and relate to others.
- Develop personal effectiveness through task-management, time-management, teamwork and group interaction, dealing with uncertainty and adapting to new situations, personal and intellectual autonomy through independent learning.
- Develop practical/technical skills such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and interpretation of data, programming of statistical packages.
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Reading List
Gary has written two textbooks (one of which is a book of solved exercises) which provide much more detail (and computer code in the case of the book of solved exercises) for most of the topics covered in the course:
Koop, G. (2003). Bayesian Econometrics, published by Wiley.
Chan, J., Koop, G., Poirier, D. and Tobias, J. (2019). Bayesian Econometric Methods, second edition, Cambridge University Press.
On the website associated with this course, we provide copies of various readings (which can be downloaded freely) of teaching handouts/monographs on relevant topics. These include:
Blake, A. and Mumtaz, H. (2017). Applied Bayesian Econometrics for Central Bankers. Bank of England working paper.
Dieppe, A., Legrand, R. and van Roye, B. (2016). The BEAR Toolbox, ECB Working paper No. 1934.
Koop, G. and Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics, monograph in the Foundations and Trends in Econometrics series.
Koop, G. (2016a). Bayesian Methods for Fat Data, manuscript.
Koop, G. (2016b). Bayesian Methods for Empirical Macroeconomics with Big Data, manuscript.
Huber, H. and Koop, G. (2022). Bayesian Models for Macroeconomic Forecasting, manuscript. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | |
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