Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
This course is an introduction to the mathematical concepts needed to read and produce economics research. The aim is to develop the basic methodological skills required to analyze and apply economic theory and econometric techniques to the problems they will study throughout the program. The key topics covered include:
- Analysis
- Constrained and Unconstrained Optimization
- Probability
Credits: 3
Schedule: 17.08.2020 - 27.08.2020
Teacher in charge (valid 01.08.2020-31.07.2022):
Teacher in charge (applies in this implementation): Daniel Hauser
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
- Analysis: Implicit function theorem, convex/concave functions, fixed point theory, separating hyperplanes, envelope theorem
- Optimization: Unconstrained optimization (1st+2nd order conditions), Con-strained Optimization (Lagrange Multiplies, Karush Kuhn Tucker Conditions), Berge's Maximum Theorem
- Probability: basic concepts (moments, independence, conditional probabil-ity), law of large numbers, central limit theorem, frequentist inference
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
100% Exam
Workload
Valid 01.08.2020-31.07.2022:
- Contact Teaching 24h
- Exercise sessions: 8h
- Exam 2h
- Independent work 47h
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Recommended textbook is Simon and Blume (Mathematics for Economists, 1994, Norton & Company)
FURTHER INFORMATION
- Teacher: Daniel Hauser