The purpose of the course is to provide participants with an understanding of dynamic programming (DP) models and their empirical application. The DP framework has been extensively used in economics to model sequential decision-making over time and under uncertainty. Prominent examples include saving/consumption decisions, retirement behavior, investment, labor supply/demand, housing decisions, and vehicle replacement decisions. The course will first review the theoretical concepts and computational approaches, and then focus on modern empirical applications covering both discrete and continuous decision problems, large scale problems and dynamic games.
The course will cover a range of solution and estimation methods for single agent problems such as value function iterations, policy iterations, method of endogenous gridpoints (EGM), mathematical programming with equilibrium constraints (MPEC), nested fixed point algorithm (NFXP), CCP-estimator, nested pseudo likelihood, as well as their extensions for stochastic games, including the recursive lexicographical search (RLS) method for solving dynamic games with multiple equilibria. The participants will get hands on experience with solving and estimating relatively simple models using provided MATLAB code.
Pre-requisites: Some basic knowledge of dynamic programming model and some programming experience (ideally in MATLAB).
To get the full benefit of the course through hands on experience with solving and estimating relatively simple DP models, participants are encouraged to bring their own laptops with MATLAB installed. Computers will not be provided.
NOTE to UCL students: There are limited free places at this event. If you would like to register for a free place please email firstname.lastname@example.org to join the waiting list.