GRA 4144 Applying Economic Analysis

GRA 4144 Applying Economic Analysis

Course code: 
GRA 4144
Department: 
Economics
Credits: 
6
Course coordinator: 
Leif Helland
Christian Riis
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2020 Spring
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

Firm managers routinely face situations in which valuable time and effort has to be expended in thinking systematically and hard about the different courses of action that may be pursued. Often decisions are taken in environments that are uncertain, but where institutions are fixed and the actions of others can be taken as given. Managers also take actions in strategic environments where the final outcome depends on the beliefs and actions of others and where this is common knowledge. In particular, managers will encounter situations in which they can impact on the rules of the game by creating incentives that shape the behavior of others. Economic analysis works out principles for rational decision-making in varying economic environments. Good management requires a thorough understanding of such principles, as well as a sensitivity to the challenges of implementing the principles.

This course introduces students to economic analysis as a tool for making decisions in a multitude of situations that are at the core of running of a firm. In support of lectures students will solve business cases; discover economic principles at work through participation in teaching experiments (either on the internet platform "Veconlab" or in the BI research lab); and work on computational tools that can be used directly in support of actual decision making. The course draws on recent advances in behavioral- and experimental economics to investigate the robustness and empirical content of economic analysis.

Learning outcomes - Knowledge

After completing the course students should:

  • have knowledge of a relevant body of research in behavioral- and
    experimental economics.
Learning outcomes - Skills

After completing the course students should be able to: 

  • Identify the main classes of economic problems encountered by the firm.
  • Mathematically formulate and solve economic models of relevance to the firm.
  • Use a combination of economic analysis, computational tools and available data in support of actual firm decision making.
  • Analyse individual managerial decisions under risk and uncertainty.
  • Analyse strategic management decisions in static and dynamic contexts.
  • Analyse strategic management decisions in the presence of imperfect and incomplete information.
General Competence

After completing the course students should be able to reflect meaningfully on questions relating
to the empirical content and robustness of economic analysis.

Course content

Different learning methods will be used in the course. These include lectures; case-work; partici-
pation in teaching experiments; and development of computational tools in support of managerial
decision making. Case-work will be discussed in class; there will be individual feedback on com-
putational tools and on performance in teaching experiments.

The course will cover the following topics:

  • Design of incentive systems (personell economics)
  • Optimal contract design
  • Bargaining for the allocation of scarce resources within and across firms
  • Investments and the hold up problem
  • Determination of production quantities and prices in different market structures
  • Network externalities and the economics of the internet
  • The relationship between ownership and incentives
  • Developing and using computational tools in support of actual decision-making (Excel and Python)
  • Insights from behavioral- and experimental economics
Teaching and learning activities

Different learning methods will be used in the course. These include lectures; case-work; partici-
pation in teaching experiments; and development of computational tools in support of managerial
decision making. Case-work will be discussed in class; there will be individual feedback on com-
putational tools and on performance in teaching experiments.

Computer-based tools: Excel, VeconLab.

Software tools
Software defined under the section "Teaching and learning activities".
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.

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:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41441
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
100Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Written examination under supervision.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
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: GRA 41441
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
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.