JEM184 - New Keynesian DSGE Modelling

Credit: 6
Status: CFS - elective
ET - mandatory
F,FM and B - elective
Masters - all
MEF - elective
Semester - winter
Course supervisors: Mgr. Jakub Bechný
Mgr. Jan Žáček
Course homepage: JEM184
Literature: Required literature:
There is no required literature. Lectures will be based on lecturer’s notes and presentations. Reference will be given when needed.

Recommended literature:
- DeJong, D. N., & Dave, C. (2011). Structural macroeconometrics. Princeton University Press, Edition 2.
- Galí, J. (2015). Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework and Its Applications. Princeton University Press, Edition 2.
- McCandless, G. (2008). The ABCs of RBCs: An Introduction to Dynamic Macroeconomic Models. Harvard University Press.
- Pfeifer, J. (2018). A Guide to Specifying Observation Equations for the Estimation of DSGE Models. Unpublished manuscript, 1-81.
- Sheen, J., & Wang, B. Z. (2016). Assessing labor market frictions in a small open economy. Journal of Macroeconomics, 48, 231-251.
- Wickens, M. (2012). Macroeconomic theory: A Dynamic General Equilibrium Approach. Princeton University Press.
Description: The course is intended for master students with orientation in economic modelling. The course will introduce dynamic stochastic general equilibrium (DSGE) models as well as the essential tools needed for their construction and implementation.

The course employs a practical and very intensive approach to model building. Students will build DSGE models step-by-step: from households’ optimisation and the firm’s problem, through equilibrium to monetary authority. Students will acquire practical hands-on experience in building DSGE models which will allow them to develop and implement their versions of DSGE models using Matlab and Dynare toolbox.

The course is of a ‘workshop’ style. There is no clear division between lectures and seminars. Students will be introduced into the theoretical background of modelling and derivation of models’ building blocks. Students will also get an opportunity to implement acquired knowledge in Matlab-based practice sessions. Moreover, several guest lectures will demonstrate the practical implementation of DSGE models in policy analysis.

By the end of the course, students should acquire knowledge of and hands-on experience in DSGE modelling. More specifically, students should be able to:
- understand the basic structure of DSGE models;
- formulate and derive the equations of standard DSGE models;
- approximate equilibrium conditions of DSGE models using the log-linearisation technique;
- calibrate, identify and diagnose DSGE models;
- implement DSGE models using Matlab, and Dynare and IRIS toolboxes;
- report and interpret the results;
- perform sensitivity and robustness analysis;
- prepare the database and estimate DSGE models;
- run filter and do historical decompositions.




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