Economics

Course Outline The course is designed to be highly hands-on, combining essential theoretical elements with extensive applications and exercises to be carried out both in class and at home. Introduction Motivation and role of DSGE models in modern macroeconomics. Review of dynamic optimization and general equilibrium concepts. Solving DSGE Models First-order (linear) approximation. Higher-order approximations: motivation and economic interpretation. DSGE Models and Data Stylized business cycle facts. Replicating business cycle features with a basic RBC model. Introduction to DSGE model estimation: Bayesian and maximum likelihood methods. Medium-Scale DSGE Models Model structure, calibration, and estimation. Shock analysis and propagation mechanisms. Discussion of limitations and possible extensions of DSGE models. Practical Applications (Lab) Implementation in Matlab or Dynare. Simulation, sensitivity analysis, and comparison with empirical data.

Students are expected to have: A solid background in macroeconomics at the master’s level, including familiarity with dynamic optimization and general equilibrium analysis. Basic programming skills in Matlab, Python, or another computational environment suitable for solving and estimating macroeconomic models.

Course materials provided in class "Structural Macroeconometrics" De Jong, David, and Chetan Dave. 2011. Princeton. University Press "Methods for Applied Macroeconomic Research" Canova, Fabio. 2011. Princeton. University Press

Attendance is not mandatory but strongly recommended

Details on exam and assessment will be provided in class.

Each lecture will cover theoretical concepts and practical applications, with assignments to be completed both during class and independently at home.