ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Advanced Topics in Microeconometrics
Code ULMR808
Coordinator Dr B Murakozy
Economics
Balazs.Murakozy@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 8 FHEQ Second Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

   6

       30
Timetable (if known)              
Private Study 120
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual project. Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes  0    40       
Individual presentation. Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No  30    30       
5 individual homework assignments Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No  0    30       

Aims

This module aims to:

1. Improve students’ data skills.

2. Equip students with state-of the art methods of causal inference.

3. Familiarize students with a number of recent papers in Applied Economics.

4. Prepare students to critically evaluate and communicate current research results.

Learning Outcomes

(LO1) Students will be able to derive empirically testable predictions from theoretical frameworks.

(LO2) Students will be able to prepare data for analysis.

(LO3) Students will be able to effectively interpret and communicate their empirical results.

(LO4) Students will be able to apply a number of methods to identify causal effects.

(LO5) Students will be able to investigate the strengths and limitations of these models and identify the best approach given the data.

(LO6) Students will be able to critically evaluate research papers in applied economics.

(S1) Research skills.
Students will develop research skills during lectures and reading journal articles.

(S2) Problem solving.
Students will develop problem solving skills through their assessments.

(S3) IT skills.
Students will develop IT skills through working with data and writing code for Econometric Analysis.

(S4) Numeracy.
Students will develop numeracy through computation in the project.

(S5) Organisation and ability to work under pressure.
Students will develop this skill during the assessments.

(S6) Presentation skills.
Students will develop presentation skills through the presentation assessment aspect of the module.

Teaching and Learning Strategies

Lectures x 24 hours (120 minutes per week).
During lectures students will present a paper of their choice; this will form part of the module's assessment. Materials for all the sessions are available online. Lectures will be recorded.

Tutorial x 6 hours (60 minutes every second week)
Tutorials will focus on coding exercises. Codes and data will be posted on Canvas.

Self-directed learning x 120 hours
Students should make use of their self-directed learning time to read the module textbooks and journal articles, work through examples, solve problems, work on the assessments, and complete exercises in addition to those covered in learning materials.

Syllabus
 

Indicative syllabus:

The module will cover a selection of the following topics:

1. Review of probability and regression

2. Potential outcomes model

3. Econometric analysis of RCTs

4. Matching and subclassification

5. Regression discontinuity

6. Instrumental variables

7. Panel data

8. Difference-in-differences

9. Synthetic Control

10. Non-linear models

11. Prediction

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.