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 Applied Microeconometrics
Code ECON826
Coordinator Professor IC Burn
Economics
Ian.Burn@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 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 20

 5

         25
Timetable (if known)              
Private Study 125
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Examination. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment schedule: semester 2  2    75       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             

Aims

This module seeks to teach students to become a critical consumer of the empirical work in existing literature. The goal is for students to learn to discuss, critique, and analyse applied economics research. The material in this course will provide students with the techniques needed to conduct their own original research in microeconomics.

Learning Outcomes

(LO1) Students will be able to discuss the methods economists use to obtain causal identification.

(LO2) Students will be able to code basic statistical analyses in STATA.

(LO3) Students will be able to critique various research methods.

(LO4) Students will be able to assess the validity and plausibility of assumptions needed for results to be causal.

(S1) Problem solving.
Lectures and coursework provide problem sets designed to teach problem solving.

(S2) IT skills.
Coursework requires students to learn to code in STATA.

(S3) Numeracy.
Lectures, coursework, and the exam teach students the mathematics of causal identification.

(S4) International awareness.
Lectures and coursework teach students to analyse research and assess the efficacy of policies.

Teaching and Learning Strategies

2 hour lecture x 10 weeks
1 hour seminar x 5 weeks
125 hours self-directed learning

Self-directed learning hours will be used for course readings and for learning how to code.

Syllabus
 

1. Empirical methods:

Experimental methods;
Regression discontinuity ;
Instrumental variables;
Differences in Differences and fixed effects;
Matching and synthetic control.

2. Data Issues:

Clustering;
Weighting;
Measurement error;
Bootstrapping.

3. Non-linear models:

Censored and truncated data;
Binary data;
Machine learning.

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