Mathematical Methods for Economists
Level:
Master's Course Status:
Core CEU code:
ECON 5010 - Mathematical Methods for Economists CEU credits:
3 ECTS credits:
6 Term:
1st Academic year:
2009/2010 Academic year:
2010/2011 Semester:
Fall Host Unit:
Department of Economics Academic Program:
Master of Arts in Economics CEU Instructor(s):
Katrin Rabitsch Learning Outcomes:
Students are comfortable with solving basic
difference equations and differential equations (or systems thereof). They have also
familiarized with the theory on dynamic optimization in discrete or continuous time and
applied these methods to simple examples of dynamic models. Assessment :
There will be weekly individualized assignments during the term
with a total of 50% weight in the assessment, and a final examination with also
50% weight. In order to get the minimum pass grade (C+) for the course, at
least 50% of the marks must be obtained in both components of the assessment
(assignments and final exam). Full description:
The course covers the mathematical methods required for the study
of modern dynamic economic models.
The main aim of this course is to provide the
students with the necessary tools for understanding, solving and analyzing modern
dynamic economic models. Emphasis will be mostly on conceptual understanding and
applications of standard tools used in the core graduate courses.
Assumed Background
Basic knowledge of Linear Algebra, Calculus, Analysis as covered in the pre-session
Mathematics course or as covered in undergraduate Mathematical Economics textbooks
such as Simon, Carl P. and Lawrence Blume: Mathematics for Economists, W.W.
Norton, First Edition, 1994 or Chiang, Alpha C.: Fundamental Methods of Mathematical
Economics, McGraw-Hill, Third Edition, 1984.
Preliminary Syllabus
• Introduction and review of important concepts: approximations and Taylor
expansions, log-linearization, eigenvalues and eigenvectors
• Linear Difference equations and systems of linear difference equations
• Applications, discrete-time models
• Ordinary Differential equations and systems of differential equations
• Qualitative theory and stability, phase diagrams
• Applications, continuous time models
• Deterministic dynamic programming
• Markov Processes
• Stochastic dynamic programming
• Applications to dynamic programming
• Optimal control (continuous time), Hamiltonians
