Computational Economics

Summer 2023

The objective of this course is to provide and equip students with the computational tools necessary to solve dynamic and stochastic models. We will pay particular attention to classic macroeconomic problems, although the tools presented in this course can be used in similar problems studied in various fields of economics.

Syllabus

 You can find the syllabus here.

Course Learning Objectives

Upon successful completion, students will be able to:

  • Explain the neoclassical growth model and understand how we can represent this model on the computer.
  • Exploit the properties of the value function to solve this problem using the full discretization method.
  • Understand how we can approximate a function on the computer. Explain the Endogenous Grid Method as an alternative solution to the neoclassical growth model and other dynamic stochastic problems.
  • Understand the importance of root finding problems in economics and discuss different methods available. Extend the value function iteration with a root-finding algorithm to solve the neoclassical growth model and other dynamic stochastic problems.
  • Discuss and introduce computational optimization methods in economics and extend the neoclassical growth model and other dynamic stochastic problems with optimization.
  • Discuss the advantages and disadvantages of each of these methods.

FAQs

Prerequisites:

  • Dynamic Programming.
  • Python or any other programming language.

Required textbook and resources:

  • Judd, Kenneth (1988). Numerical Methods for Economists, Cambridge, MA. MITPress.
  • Ada, Jerome and Cooper, Russell W. (2003). Dynamic Economics, Cambridge, MA. MITPress.
  • Fernández-Villaverde, J., Rubio-Ramírez, J. F., and Schorfheide, F. (2016). Solution and estimation methods for DSGE models. In Handbook of macroeconomics (Vol. 2, pp. 527-724). Elsevier.
  • Class Canvas Website.
  • Zoom.
  • The code for lecture notebooks are posted at: https://github.com/salinasdcs/ssef-2023