Studying at York
Applied Microeconometrics - ECO00092M
- Department: Economics and Related Studies
- Module co-ordinator: Prof. Cheti Nicoletti
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
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 2024-25 |
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 to produce prediction, run heterogeneity analysis and improve estimation of causal effects.
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.
Assessment
| Task | Length | % of module mark |
|---|---|---|
| Essay/coursework Applied Microeconometrics |
N/A | 100 |
Special assessment rules
None
Additional assessment information
The assessment will be a take home project for which the student will be given a weeks time. The project will involve an empirical analysis using Stata, writing up of the results and critical interpretation of estimation and tests findings.
Reassessment
| Task | Length | % of module mark |
|---|---|---|
| Essay/coursework Applied Microeconometrics |
N/A | 100 |
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. “Machine learning: An applied econometric approach,” 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.