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ECON50740

Academic Year 2025/2026

PhD Macroeconomics 1 (ECON50740)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
5 (Doctoral)
Credits:
10
Module Coordinator:
Professor Karl Whelan
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This is a PhD-level level course in macroeconomics. The principal focus is on the theoretical and empirical methods used by modern macroeconomists. The module has an applied focus, with extensive use made of Matlab to illustrate how to build and solve macroeconomic models. The second module in the sequence will focus on a range of topics related to macroeconomic policy.

Most of modern macroeconomics is focused on dynamics: How do macroeconomic variables change over time and how do the variables interact with each other? In some cases, macroeconomists design models intended to match data and be used for policy analysis. In other cases, they design ``stylised'' models designed to shed light on certain specific phenomenon or inter-relationships. Macroeconomic models also differ in other ways:
(a) Are they deterministic or stochastic?
(b) Do they use discrete time or continuous time?
(c) Do they use Lagrangian-based optimisation methods or more sophisticated techniques like dynamic programming and optimal control?

We will cover all of these model types and will use "type of model'' as our organising framework. The list of planned topics is as follows:

1. Deterministic Dynamic Models: Difference equations, differential equations, phase diagrams. optimal control.
2. Stochastic Dynamic Models: Markov chains, autoregessive models including vector autoregressions.
3. Rational Expectations Models: Stochastic difference equations with expectations, real business cycle model, DSGE models.
4. Dynamic Programming in Discrete Time: Finite horizon and infinite horizon dynamic programming in both deterministic and stochastic settings.

About this Module

Learning Outcomes:

On completion of this module, students should be able to understand most of the papers in the modern macroeconomics literature. They should also know how to formulate and simulate dynamic macroeconomic models in Matlab. With some additional work, students could contribute their own research to the field by formulating and simulating new macroeconomic models.

Indicative Module Content:

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

200
Lectures

24
Total

224

Approaches to Teaching and Learning:
The module will be taught via a series of lectures combined with a set of assignments to be done over the course of the term. Lecture notes and readings will be provided by the module co-ordinator. There will be a strong applied focus, with lectures and assignments regularly focused on getting various types of dynamic models to run in Matlab.

On the use of AI: In general, AI tools like ChatGPT will not be very helpful in helping you to do the assessments for this class.

- We will discuss the requirements for the assignments each week but I will require a unique submission that is clearly distinguishable from anyone else's work.
- AI will not write useful PhD-level discussions for the research paper reviews
- You could try to use AI for preparing answers for the final exam essay questions but, again, you are really just better off consulting the relevant parts of the lecture notes. This is because the lecture notes have been prepared by the person actually grading the exams. I am generally looking for some specific key points to be made and the lecture notes will emphasise these points. The AI tool is unlikely to provide answers that emphasise these same key points.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Final exam during examination period End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
45
 No
Quizzes/Short Exercises: Weekly assignments during the term Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
40
 No
Report(s): Reports on academic papers Week 15 Alternative linear conversion grade scale 40% No
15
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The module co-ordinator will be available to give feedback throughout via online office hours or scheduled calls.

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