GRA 6612 Analysis of Experiments and Quasi-Experiments

GRA 6612 Analysis of Experiments and Quasi-Experiments

Course code: 
GRA 6612
Department: 
Economics
Credits: 
6
Program of study: 
Master of Science in Business
Course coordinator: 
Christian Brinch
Product category: 
Master
Portfolio: 
MSc in Applied Economics
Semester: 
2020 Spring
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

This course teaches modern design based approaches to empirical analysis that are prevalent in applied microeconomics. In particular, we focus on analysis techniques associated with credible empirical evidence - with a strong leaning towards experimental and quasi-experimental techniques. The course teaches both the actual implementation of the techniques using statistical software and relevant applications in the economics literature, with a particular emphasis on business relevant topics, such as personell economics and industrial organisation, in addition to classical applications in labor economics, health economics and development economics. We also explore to what extent the analysis techniques are used and can be used in business contexts.

Learning outcomes - Knowledge

After taking this course, students should

  • know how to analyze empirical microeconomic problems using data from lab experiments or field experiments,

  • know how to analyze data using regression techniques to generate comparable samples,

  • know how to analyze data using instrumental variable techniques to overcome exogeneity problems,

  • know how to exploit regression discontinuity designs,

  • know how to analyze data using the difference-in-differences methodology.

Learning outcomes - Skills

After taking this course, students should

  • be able to perform experimental and quasi-experimental analyses of economic data using statistical software

  • be able to assess empirical analyses of economic problems, evaluating to what extent the analyses provide credible empirical evidence.

General Competence

After taking this course, students should

  • understand the selection problem and why correlation does not imply causality

  • understand how and why randomized experiments enable researchers to answer causal questions empirically and why observational studies only rarely enable researchers to do provide such answers

Course content
  • Empirical strategies in applied microeconomics
  • Comparing experimental techniques and observational studies

  • Implementation of empirical strategies in statistical software

  • Key scientific studies in business relevant economics

  • Applications of experimental techniques to business problems

Teaching and learning activities

-

Software tools
Stata
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Activity
Form of assessment:
Presentation
Exam code:
GRA66121
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No -Group (2 - 3) Presentation
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA66121
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
80Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Written examination under supervision
Exams:
Exam category:Activity
Form of assessment:Presentation
Weight:20
Invigilation:No
Grouping (size):Group (2-3)
Support materials:
Duration: -
Comment: Presentation
Exam code: GRA66121
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:80
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:Written examination under supervision
Exam code: GRA66121
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Sum workload: 
0

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.