Computational Dynamics

(1.5 Credits)

The solution of state-of-the-art quantitative models in economics and finance often requires numerical implementation on a computer. In this course, we will explore a range of computational methods used to solve such quantitative dynamic problems: Local approximations of equilibria in models for macro-policy analysis, global methods for approximating solutions to nonlinear decision problems, filtering techniques used in learning and estimation, discretization methods popular in financial economics, as well as neural network models used in data science. Students will implement each method using Python.