Topics in Empirical Economics I
This is a graduate course in empirical economics. The course will examine a number of methods in empirical economics, including applied micro-econometrics, experimental economics, economic modelling and computational economics. 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 health and environmental economics.
Full module specification
|Module title:||Topics in Empirical Economics I|
|Duration of module:||
Duration (weeks) - term 1: |
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.
ILO: Module-specific skills
- 1. Explain the causal treatment effect evaluation problems, and apply different treatment effect evaluation methods in labour, environment, and health economics research.
- 2. Discuss the literature regarding preference anomalies and to relate this to economic and psychology literatures.
- 3. Explain experimental approaches and apply them to the analysis of preferences
- 4. Explain why and how empirical economists employ resampling methods (simulation methods) such as Monte Carlo analysis, bootstrapping and cross-validation in their analyses.
- 5. Develop the coding skills required to apply resampling methods in empirical analyses
ILO: Discipline-specific skills
- 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
- 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
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled Learning and Teaching||18||Lectures (2 hours per week)|
|Guided independent study||132||Reading, preparation for classes and assessments|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Assignment 1||35||Research replication and method comparison, one 1000-word report, one computer code file for analysis, and one software output file including results||1, 6-9||Oral/Written|
|Assignment 2||30||1500 words||2,3,6-8,10||Oral/Written|
|Assignment 3||35||Application of a resampling method to a research problem: Computer code and 1,000 report interpreting the analysis performed using that code.||4-9||Oral/Written|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Assignment 1 (35%)||Resubmission||1, 6-9||August Examination Period|
|Assignment 2 (30%)||Resubmission||2,3,6-8,10||August Examination Period|
|Assignment 3 (35%)||Resubmission||4-9||August Examination Period|
i. The Evaluation of Treatment Effects
This topic covers the econometrics theory of treatment effect identification, different treatment effect measurement, and a range of estimation methods. Applications will focus on empirical contributions in labour and health economics.
ii. Experimental Insights on Non-Standard Utility Theory
A contrasting comparison of applied econometric with empirical experimental studies will be used to investigate the notion that evidence regarding non-standard (or anomalous) versus standard preferences reflects differences between so-called System 1 (fast) and System 2 (slow) modes of decision making.
iii. The Application of Resampling (Simulation) Methods in Empirical Economics
Modern economics often involves examination and understanding of complex empirical objects; for example, difficult-to-calculate statistics, competing statistical estimators, posterior probability distributions in Bayesian analysis or complex models of economic behaviour. Often this complexity means that it is not feasible to develop insights using analytical methods alone. Where that is the case, economists increasingly turn to computers to do the hard work, applying resampling (or simulation) methods to provide the desired insights. In this section of the module students will be introduced to a variety of these methods including Monte Carlo analysis, bootstrapping and cross-validation and examine their application to a variety of empirical economic problems. Students will develop the coding skills to apply these methods in their own work.
Indicative learning resources - Basic reading
- handout/lecture notes
- published academic papers suggested by the convenors
- J. M. Wooldridge. Econometric analysis of cross section and panel data. MIT press, 2010
- J. D. Angrist and J.-S. Pischke. Mostly harmless econometrics: An empiricist’s companion. Princeton university press, 2008.
- C. Cameron and P. K. Trivedi. Microeconometrics: Methods and applications. Cambridge university press, 2005.
- J. Bhattacharya, T. Hyde and P. Tu, Health Economics. Palgrave Macmillan
- D. Kahneman (2012) Thinking, Fast and Slow, Penguin books.
- Metropolis (1987). The beginning of the Monte Carol method, in Los Alamos Science, issue 16, p125-130.
- Horowitz, J.L., 2019. Bootstrap methods in econometrics. Annual Review of Economics, 11, pp.193-224.
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Last revision date