Applied Microeconometrics - ECO00092M
Module summary
The module will introduce students to modern methods in microeconometrics with a focus on causal inference and empirical examples of evaluation of real-world policies, programmes and treatments.
Module will run
Occurrence | Teaching period |
---|---|
A | Semester 2 2025-26 |
Module aims
The module is designed for students who want to learn how to use
econometric methods in practice and to interpret and understand
estimation and test results. Examples of applications will be taken
from Development, Health, Public Policy, Energy and Environment and
Labour Economics.
Causal inference: The main
part of the module will be on causal inference. You will learn tools
to establish what causes what, e.g. to evaluate the effect of
introducing energy efficiency upgrades in schools on actual energy
usage, the impact of interventions to increase public accountability
of hospitals on health, and the effect of introduction of minimum wage
on employment. You will learn what experimental and quasi-experimental
approaches for causal inference are (e.g. random control trial,
difference-in-difference and instrumental variables).
The module
will also give a short introduction on how artificial intelligence
(AI), Big Data and Machine Learning can help us with causal
inference.
Artificial Intelligence (AI): You
will learn how to use and not to use AI for research e.g. to review
estimation methods that have been used for specific empirical research
questions on causal effects.
Big Data: You will
learn what big data are and how they can help address research
questions in economics (e.g. data from administrative registers, GPS,
mobile phones, social media, google search, website extraction and
satellite pictures on night lights, farming and pollution.
Machine learning: an introduction on how machine
learning methods can help in applied research.
Stata
software: Students will learn how to use Stata to implement
estimation methods and testing procedures. The module will provide
basic coding skills in Stata that are applicable to a broad set of
coding platforms and statistical software. This will be invaluable for
any empirical MSc dissertation topic and also for any job which
involves the use of data for economic analysis.
Module learning outcomes
On completing the module a student will be able to:
interpret
regression and testing results,
understand the basics of methods
for the evaluation of causal effects,
learn how to critical
review applied papers addressing,
specific economic questions
with a focus on estimation methods,
understand the basics of
machine learning in the context of causal inference,
critically
assess empirical applications.
Indicative assessment
Task | % of module mark |
---|---|
Essay/coursework | 100.0 |
Special assessment rules
None
Additional assessment information
The assessment will be a take-home project for which the student will be given a week time. The assessment will involve reading a paper, interpreting results, discussing Stata commands that could be used to implement the main finding in the paper, plus other questions that will require critical thinking.
Indicative reassessment
Task | % of module mark |
---|---|
Essay/coursework | 100.0 |
Module feedback
Feedback will be provided in line with University policy
Indicative reading
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
Mullainathan, Sendhil and Jann Spiess. 2017. āMachine learning: An applied econometric approach,ā Journal of Economic Perspectives , 31(2): 87--106.
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using Stata (Vol.2). College Station, TX: Stata press.
Wooldridge, J. M. (2010). Econometric analysis of cross section and
panel
data. MIT press.