Computational Economics with Python

Computational methods are of crucial importance for applications in economics and beyond. This ranges from economic modelling to applied economics to econometrics to finance and data science. This seminar intends to give a broad overview over the techniques, methods, and applications. It includes programming with the general purpose programming language Python and demonstrates how computational methods are implemented in Python. It allows students to gain experience with the simple applications of computational methods in general and with Python in particular. While the economic application of sophisticated methods like  machine learning, agent-based modelling, and micro-econometrics, and natural language processing is beyond the scope of this course, it will give students a basic understanding and a good starting point for further studies in these fields.

There will be no examination. Grades will be based on homework assignments which have to be completed during the term.

1. Introductory lecture: Computational economics and Python

2. Basic programming techniques

3. Programming style and good practices

4. Data structures

5. Computational techniques: statistical and econometric analysis

6. Computational techniques: Monte Carlo simulations

7. Computational techniques: Visualization

8. Outlook: Text mining, machine learning, agent-based modelling

Recommended literature:

Wentworth, P., Elkner, J., Downey, A. B., and Meyer, C. (2012). How to think like a computer scientist: Learning with Python 3. Open Book Project: http://openbookproject.net/thinkcs/python/english3e/index.html

Complementary literature:

Elsner, W., Heinrich, T., and Schwardt, H. (2015). Microeconomics of Complex Economies: Evolutionary, Institutional, Neoclassical, and Complexity Perspectives. Academic Press, Amsterdam, NL, San Diego, CA, et al.

Sargent, T. and Stachurski, J. (2020). Quantitative economics with Python. Online version: https://python.quantecon.org/