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DRPS : Course Catalogue : School of Economics : Economics

Postgraduate Course: Bayesian Econometrics (ECNM11060)

Course Outline
SchoolSchool of Economics CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryBayesian Econometrics builds on core econometric modules to develop a Bayesian approach to econometrics with applications in modern macroeconomics. Computational methods are developed alongside theory.
Course description Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and seeing 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. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions (VARs), time-varying parameter VARs (TVP-VARs) and factor models. Empirical illustrations that show how these models can be used to address macroeconomic questions will be provided throughout the course.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  0
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 )
Additional Information (Learning and Teaching) Project work 30 hours
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Journal article review 50%, Empirical project 50%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. 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.
  2. Demonstrate and develop research and investigative skills such as problem framing and solving and the ability to assemble and evaluate complex evidence and arguments.
  3. Develop communication skills in order to critique, create and communicate understanding and to collaborate with and relate to others.
  4. 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.
  5. Develope practical/technical skills such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and interpretation of data, programming of statistical packages.
Reading List
Koop, G. (2003). Bayesian Econometrics, published by Wiley.
Koop, G. and Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics, monograph in the Foundations and Trends in Econometrics series.

Additional Information
Graduate Attributes and Skills Not entered
Additional Class Delivery Information 14 hours of lectures plus 4 hours of computer tutorials
KeywordsNot entered
Course organiserDr Tatiana Kornienko
Tel: 0131 650 8338
Course secretaryMs Grace Oliver
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