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ECON50730

Academic Year 2024/2025

PhD Macroeconomics 2 (ECON50730)

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

Curricular information is subject to change.

The second PhD Macro course complements the first and covers a selection of current topics in macroeconomics research and policy analysis, as well as long-run growth.

The module has two parts. The first part covers growth theory. It presents classical models of exogenous and endogenous growth, as well as a broad empirical literature that tests some of the key predictions of the endogenous growth literature.

The second part covers a selection of current topics in short-run macroeconomics research and policy. It mainly focuses on empirical work that employs cross-sectional heterogeneity to study the effects of economic and financial shocks of interest to macroeconomists. This part covers topics at the intersection of macroeconomics, finance, monetary policy and banking.


About this Module

Learning Outcomes:

The learning outcomes of the module are (I) developing an understanding of mainstream tools and models used to conduct macroeconomic analysis and research (II) the ability to understand and critically evaluate recent advancements in macroeconomic research; (III) the ability to generate research ideas and conduct macroeconomic research.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

200
Lectures

25
Total

225

Approaches to Teaching and Learning:
Lectures, enquiry & problem-based learning.

Policy on the use of Generative AI: Generative AI, such as ChatGPT, may be used in this module in the following ways:

• To understand main concepts/theories and find definitions.
• To correct grammar and improve the writing style of your own work.
• For technical assistance, such as instructions on how to use a statistical software.

Please note the following:
1. The use of AI-generated content without explicit permission and attribution is a form of academic misconduct. Plagiarism risk is high when using AI, and it is not acceptable to submit AI-generated content as it is. If you copy and paste entire AI-generated answers, it will be considered plagiarism. Even AI co-created work is considered collusion if you present it all as your own.

2. You are therefore required to explicitly state whether AI was used in the assignment and how.

3. You should also check all information provided by AI for accuracy. Be sure that you can verify all generated text, again, do NOT simply copy/paste the AI-generated information into your assignments.

4. If academic misconduct is suspected, you may be asked to discuss or explain (verbally and without prior notice) components of your assignment to determine the authenticity of the work.

Requirements, Exclusions and Recommendations
Learning Requirements:

No requirements


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
Assignment(Including Essay): Continuous assessment Week 8, Week 12 Alternative linear conversion grade scale 40% No
40
 No
Exam (In-person): Final exam Week 15 Alternative linear conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

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

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33 Wed 16:00 - 17:50
Spring Lecture Offering 1 Week(s) - 29 Wed 16:00 - 17:50
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