Research Methods II
This module will cover a broad range of tools to analyse data: programming, program evaluation, structural estimation and machine learning.
Full module specification
|Module title:||Research Methods II|
|Duration of module:||
Duration (weeks) - term 2: |
The module goal is to equip students with a set of tools that allows them to analyse economic data in an appropriate way.
ILO: Module-specific skills
- 1. explain the main approaches to data analysis
- 2. use different approaches to data analysis
ILO: Discipline-specific skills
- 3. explain how to use different software to analyse economic data
- 4. assess the difference between program evaluation techniques and structural estimation in economics
ILO: Personal and key skills
- 5. identify the relevant research methods to analyze data
- 6. work independently and responsibly on data analysis
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||22||Lectures|
|Scheduled Learning and Teaching||10||Tutorials|
|Independent Study||118||Independent study|
|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|
|4 Problem Sets||45||1-4 Problems each||1-6||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|
|Examination||Examination 55% (3 hours)||1-6||August examination period|
|Problem Sets||Problem set 45% (1-4 problems)||1-6||August examination period|
ii. Program evaluation
iii. Structural estimation
iv. Machine learning
Indicative learning resources - Basic reading
-Adams, A., Clarke, D., and Quinn, S. (2016) Microeconometrics and MATLAB: An Introduction, Oxford University Press.
-Angrist, J., and Pischke, J-S. (2009) Mostly Harmless Econometrics. Princeton University Press.
-Athey, S., and Imbens, G. (2017) “The State of Applied Econometrics: Causality and Policy Evaluation,” Journal of Economic Perspectives, 31(2):3-32.
-Baum, C. (2016) An Introduction to Stata programming, Stata Press.
-Low, H., and Meghir, C. (2017) “The Use of Structural Models in Econometrics,” Journal of Economic Perspectives, 31(2):33-58.
-Mullainathan, S. and Spiess, J. (2017) “Machine Learning: An Applied Econometric Approach,” Journal of Economic Perspectives, 31(2):87-106.
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Last revision date