Date and Time:
Monday and Friday, 12:00-1:15pm
Wednesday, 4:30-5:45pm (Please sign-up for a particular slot.)
This course consists of two parts. The first part introduces tools for solving and estimating linearized, full-information, dynamic stochastic general equilibrium (DSGE) models. Students will develop tools in matlab to solve and estimate medium-scale DSGE models using Bayesian methods. Part two of the course explores alternatives to the linearized, full-information, rational expectations paradigm. Students will write a final paper which incorporates at least one of these alternatives.
Linked here. The syllabus includes an updated list of readings.
Problem Set 3 (due October 26th) Simulated Data; Bootstrap Code; Related example code, solutions (Logon via Blackboard)
Solving the RBC model with a stochastic trend, example code.