ECON50710 PhD Econometrics 1

Academic Year 2022/2023

Good empirical economics is a combination of asking an interesting research question and finding an empirical approach – an identification strategy – that enables you to answer the question.

The aim of this module is to equip you with advanced econometric methods for cross-section and panel data methods used for identification in current research using micro data. It provides a firm theoretical grounding in estimation and inference along with practical applications.



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Curricular information is subject to change

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)
Fixed effect models
Matching
Differences-in-differences

Time permitting: Quantile regression, Discrete choice, GMM

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning

175

Lectures

24

Total

199

Approaches to Teaching and Learning:
Task-based learning (in problem sets)
Group work (in problem sets)
Problem-based learning (in replication exercise)
Critical reflection (in referee report)
Presentation 
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  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Referee Report & presentation Varies over the Trimester n/a Graded No

25

Continuous Assessment: Problem sets Throughout the Trimester n/a Graded No

24

Examination: Final exam - end of semester Unspecified No Graded No

21

Assignment: Replication exercise Varies over the Trimester n/a Graded No

30


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring No
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.

Name Role
Dr Benjamin Elsner Tutor
Giacomo lo Conte Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
     
Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 16:00 - 17:50