• University of Exeter Business School
• Module

Topics in Empirical Economics I

Module description

This is a graduate course in empirical economics. The course will examine a number of methods in empirical economics, including applied micro-econometrics, economic modelling and computational techniques. Students will be introduced to seminal and recent advances in these empirical methods with applications drawn from across the economics discipline but with particular emphasis on development and environmental economics.

Module aims - intentions of the module

The module has two main aims: first, to equip students with the toolkit necessary to critically assess research contributions in empirical economics; second, to inspire students to answer their own research questions using different methods of empirical economic analysis.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Study the problem of causal inference in economics and the relative established techniques in estimating treatment effects: instrumental variables, randomized controlled trials, matching, difference-in-differences, synthetic control and regression discontinuity design.
• 2. Explain why and how empirical economists employ resampling methods and simulation methods such as Monte Carlo analysis, bootstrapping and cross-validation in their analyses.
• 3. Explain the functionality of machine learning techniques such as penalized regression, regression trees, random forests and neural networks.
• 4. Apply machine learning methods to high-dimensional datasets, structural equations and treatment effects
• 5. Develop the coding skills required to apply the above methods in empirical analyses

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 6. acquire advanced understanding of important methods in empirical economics and to obtain the skills to apply those methods in original research;
• 7. develop self-direction and originality in solving research problems using methods of empirical economics.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 8. work independently and effectively in solving complex research problems.
• 9. use computers to explore and solve difficult empirical research problems.
• 10. present work to different audiences ranging from the innovative contribution to knowledge emphasised by the academic community to the concise summaries required by policy stakeholders

Syllabus plan

The course aims to provide graduate students a comprehensive set of econometric tools widely used in modern empirical research. The course will provide an overview of different empirical methods and their practical implementation in R. The course will start with the basics of structural equations and potential outcomes, as well as simulation-based and resampling methods. The focus will then be shifted towards causal inference and treatment effects, presenting advanced techniques in instrumental variables, randomized controlled trials, matching, difference-in-differences, synthetic control and regression discontinuity design. The last part of the course will present various machine learning techniques and their applications to structural equations and treatment effects. Applications of the methods will be discussed throughout the course, particularly in development and environmental economics. 

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Indicative learning resources - Basic reading

Compulsory:

• handout/lecture notes
• suggested published academic papers

Optional:

• J. M. Wooldridge. Econometric analysis of cross section and panel data.  MIT press, 2010
• S. Cunningham. Causal Inference. The mixtape. Yale University Press, 2021-09-14
• J. Friedman, T. Hastie, R. Tibshirani. The elements of statistical learning. Springer series in statistics, 2001
• J. D. Angrist and J.-S. Pischke. Mostly harmless econometrics:  An empiricist’s companion. Princeton university press, 2008.

Key words search

Applied Econometrics, Causal Inference, Machine Learning