Date and Time:
Tuesday and Thursday, 12:00-1:15pm
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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.
Solving the linearized RBC model with a stochastic trend, example code.
A second version of the same model, using matlab to output a non-symbolic file for fast evaluation.
A third version of the same model, with agents whose expectations are generated by statistical learning.
A monetary model with regime switching in the monetary policy rule.