Contents & Objectives:
This course provides state of the art techniques for quantitative macroeconomic research. Students will learn about the most prominent models in this field and familiarize with the relevant literature. At the end of the semester, they are guided to develop their own ideas and conduct policy analysis using the techniques acquired in class. The course will consist of a series of lectures and tutorials and is divided into three parts:
1. Basic concepts of programming in FORTRAN and standard numerical methods are reviewed.
2. Introduction of solution techniques for dynamic programming problems.
3. Dynamic programming techniques are applied to stochastic growth and life-cycle models.
Advanced Computational Economics (summer term 2021)
Room 414, October 11th – October 22nd 2021
Due to COVID-19 the MA course „Advanced Computational Economics“ will be taught within two weeks in October. During the course, students have to sit in distance with a mask. In the following, I will explain the course content, structure and timeline, the course material, as well as requirements to complete the course successfully.
This course has no mandatory requirements. Nevertheless, previous exposure to programming and participation in the undergraduate class Computational Economics are beneficial.
The course combines seven days (11.-19.10) of intensive lectures and exercise classes. Students need to bring their own computer to lectures and exercise classes. In the first lecture you will install the free programming language FORTRAN on your computer. Then you will learn to write code and solve simple numerical exercises in FORTRAN. On this basis we can study the theoretical structure of various economic models and implement these models in FORTRAN.
Over the weekend, you have to prepare an assignment where you write a code in FORTRAN in a team consisting of not more than two members. When the exercises are finished on Tuesday, there are two days (20/21.10) to prepare for the final exercise and/or come up with questions. On Friday (22.10) the last day of the course, you have to solve a programming exercise by yourself. In this exercise, you will receive a FORTRAN code, which contains a number of programming errors. You have to correct these bugs and run the program. The programming exercise as well as the team assignment are both graded. The average of both marks defines your final grade. However, you need to pass both tests in order to complete the course successfully.
Daily time schedule for classes:
9:00-10:30 and 11:00-12:30 - Lecture class, where the specific material is explained.
14:00-17:00 - Exercise class where you mostly programme by yourself.
It is absolutely necessary to attend the lecture classes in order to manage the exercises! If there is an exam on a specific day, we will try to reorganize the schedule. Every participant will receive a set of Lecture Notes free of charge in the first lecture. The material there can (and should) be studied before attending the respective lectures.
The programming exercise on the last day will last for 90 minutes from 9:00-10:30.
Detailed course plan:
Monday: Introduction to programming with Fortran 90
Tuesday: Numerical solution methods: Nonlinear equation systems, function minimization and approximation
Wednesday: Introduction to dynamic programming: Basic concept, value function and policy function iteration
Thursday: Dynamic Macro I: Infinite horizon models: Neoclassical growth
Friday: Dynamic Macro I: Infinite horizon models: Stochastic growth and RBC models
Weekend: Prepare teamwork assignment
Monday: Life cycle models and risk: Labour supply and savings
Tuesday: Life cycle models and risk: Portfolio choice
Wednesday and Thursday: Prepare for programming exercise
Friday: Programming exercise
Advanced study or preparation:
In case you want to prepare in advance or study additional material, you can consult selected chapters of the following book, which you can find in the library:
Hans Fehr and Fabian Kindermann (2018): Introduction to computational economics using Fortran, Oxford: Oxford University Press.