ECON50710

Learning Outcomes:

By the end of this course, you should be able to:
• Assess critically the empirical methods used in the analysis of micro data sets
• Apply the appropriate econometric techniques to own research
• Interpret econometric output from software packages
• Theoretically derive linear and non-linear estimators
• Communicate research results appropriately in written and oral formats

Indicative Module Content:

The course covers the following topics:

OLS estimation
Statistical inference (including simulation-based approaches)
The Design Approach to Econometrics
Instrumental Variables
Applications to empirical problems

Student Effort Hours:

Approaches to Teaching and Learning:

Task-based learning (in problem sets)
Group work (in problem sets)
Critical reflection (in referee report)
Presentation

Students may use AI for help with programming, literature search, and data search, However, students must write the referee report without assistance from AI. It is considered plagiarism to have AI write any sections of the referee report. Please note that if academic misconduct is suspected, you may be asked to discuss or explain components of your assignment to determine the authenticity of the work.

Requirements, Exclusions and Recommendations

Learning Requirements:

This course requires completion of a M.Sc-level course in econometrics and/or statistics. Students should be familiar with matrix algebra.

Module Requisites and Incompatibles

Not applicable to this module.
 

Assessment Strategy

 

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?

Not yet recorded.