Date and Time:
Wednesday, 9h30 - 12h30
Mondays 15h45 - 17h00
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The first part of this course introduces some basic tools for solving and estimating linearized, full-information, dynamic-stochastic general equilibrium (DSGE) models. Coursework consists of three problem sets with a heavy computational emphasis. You will spend a great deal of time programming in matlab. After completing these problem sets, each student will have an extensive ``toolbox" of programs that she can use to address empirical questions in a structural manner.
Linked here. The syllabus includes a (continuously 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.