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:






Duration of module: Duration (weeks) - term 2:


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 ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching22Lectures
Scheduled Learning and Teaching10Tutorials
Independent Study118Independent study

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Practice ProblemsVaries1-6Oral/Written

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Exam553 hours1-6Oral/Written
4 Problem Sets451-4 Problems each1-6Oral/Written

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
ExaminationExamination 55% (3 hours)1-6August examination period
Problem SetsProblem set 45% (1-4 problems)1-6August 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.

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