Research Methods II
Module description
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 |
|---|---|
| Module code: | BEEM143 |
| Module level: | M |
| Academic year: | 2020/1 |
| Module lecturers: | |
| Module credit: | 15 |
| ECTS value: | 7.5 |
| Pre-requisites: | BEEM136 |
| Co-requisites: | None |
| Duration of module: |
Duration (weeks) - term 2: 11 |
Module aims
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 |
|---|---|---|
| 32 | 118 | 0 |
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 |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Practice Problems | Varies | 1-6 | Oral/Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 45 | 55 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Exam | 55 | 3 hours | 1-6 | Oral/Written |
| 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 |
Syllabus plan
i. Programming
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.
Module has an active ELE page?
Yes
Origin date
24/06/2019
Last revision date
20/08/2019