Modules | Management School | University of Liverpool

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	ECON508
Coordinator	Dr B Murakozy Economics Balazs.Murakozy@liverpool.ac.uk
Year	CATS Level	Semester	CATS Value
Session 2025-26	Level 7 FHEQ	Second Semester	20

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							170
TOTAL HOURS							200

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 Homework Assignments Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No	0		30
Individual Presentation Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No	30		30
Individual Project Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes	0		40

Aims

This module aims to:

Improve students’ data skills;

Equip students with state-of the art methods of causal inference;

Familiarise students with a number of recent papers in applied economics;

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) Organisation skills This is relevant for planning scheduled work and meeting assessment deadlines. This will be evident in the students’ independent management of their assignments and coursework and by meeting coursework deadlines.
(S2) Problem solving and decision making skills Students will be challenged to think critically about microeconomic models. They will do this by gathering and synthesising information, analysing alternative perspectives and options and presenting a considered opinion or course of action in their course assessment.
(S3) Digital 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) Communication skills Students will develop verbal and oral communication skills through the assessment aspect of the module.
(S7) Ethical awareness Students will enhance their ethical awareness by reflecting how research including their own is consistent with the principles of research ethics, integrity and open science and learning how to resolve issues and navigate challenges through in class work.
(S8) International awareness International issues will be covered in class work.

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 will be available via the VLE. 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 170 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, and reflect about the implications for their own research.

Syllabus

Indicative syllabus:

Review of probability and regression

Potential outcomes model

Econometric analysis of RCTs

Matching and subclassification

Regression discontinuity

Instrumental variables

Panel data

Difference-in-differences

Synthetic Control

Non-linear models

Prediction

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