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Instructors: Prof. Dr. Olaf Posch
Event type:
Interactive class
Displayed in timetable as:
Hours per week:
3
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
- | 45
Comments/contents:
Many interesting economic models cannot be solved analytically. This course introduces computational methods in order to solve dynamic stochastic nonlinear economic models. It illustrates that most numerical methods exploit the recursive structure of dynamic economic models. This course does not make any attempt to cover the numerous numerical techniques or potential economic applications. It instead chooses to develop only a relatively small number of techniques that can be applied easily to a wide variety of economic problems. It also demonstrates how to use some of the available toolkits.
Learning objectives:
After this course, the students are able to
- explain different approaches of solving dynamic optimization problems by means of numerical approximation techniques
- categorize the numerical techniques covered in the lecture
- solve a given dynamic problem by applying a reasonable numerical technique
- apply a toolbox in order to solve dynamic general equilibrium models
- formulate and solve dynamic problems in discrete and continuous time
Didactic concept:
- this course emphasizes practical numerical methods, not mathematical proofs
- focus on techniques that is particularly useful in economics
- the examples in this lecture cover a wide range of models in economics
- provide working examples and use the CompEcon toolbox
- the students program in Matlab by themselves and develop their own computational economic applications
Computational Economics: Interactive lecture (3 SWS)
- Matlab primer: introduction, conditional statements and looping, scripts and functions, debugging and programming style
- Basic numerical methods: linear and nonlinear equation methods, complementarity methods, finite-dimensional optimization, numerical integration and differentiation, and function approximation
- Methods for solving dynamic stochastic models: dynamic programming, rational expectations in discrete and continuous time
Remark: Students need to participate actively and self-study throughout the course.
Literature:
supplementary:
- Heer, Burkhard and Alfred Maussner, Dynamic General Equilibrium Modelling - Computational Methods and Applications, Springer, 2nd edition, 2009.
Additional examination information:
There will be four problem sets with the possibility of improving the final grade of the exam by 0.3/0.4 (good performance) or 0.6/0.7 (exceptional performance) points. The minimum number of solved problems sets must be three out of the four problem sets (respecting the deadlines) in order to qualify for the grade improvement. Handing in the problem sets is not required to take the exam.
Please note that the exam will take place in the form of of a take-home exam. All participants have 90 minutes to answer the exam questions and will be given additional time to upload the required documents on a shared folder. There will be the possibility for clarifying questions. Please make sure that your programs are properly installed and ready to use as there will be no technical assistence during the exam.
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