Research Methods II
| Module title | Research Methods II |
|---|---|
| Module code | BEEM143 |
| Academic year | 2019/0 |
| Credits | 15 |
| Module staff |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 12 |
|---|
Module description
This module will cover a broad range of tools to analyse data: programming, program evaluation, structural estimation and machine learning.
Module aims - intentions of the module
The module goal is to equip students with a set of tools that allows them to analyse economic data in an appropriate way.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. explain the main approaches to data analysis
- 2. use different approaches to data analysis
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 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
On successfully completing the module you will be able to...
- 5. identify the relevant research methods to analyze data
- 6. work independently and responsibly on data analysis
Syllabus plan
i. Programming
ii. Program evaluation
iii. Structural estimation
iv. Machine learning
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 |
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.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | BEEM136 |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 24/06/2019 |
| Last revision date | 20/08/2019 |