Semestr - zimní
|Garanti:|| Ing. Aleš Maršál
|Literatura:||1) Galí J.: Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework, ISBN: 9780691133164
2) McCandless G. The ABCs of RBCs: An Introduction to Dynamic Macroeconomic Models, ISBN-10: 0674028147
3) Bernanke, B. S., M. Gertler, & S. Gilchrist (1999): The financial accelerator in a quantitative business cycle framework." In J. B. Taylor & M. Woodford (editors), Handbook of Macroeconomics, volume 1 of Handbook of Macroeconomics, chapter 21, pp. 1341-1393. Elsevier.
4) Christiano, L. J., M. Trabandt, & K. Walentin (2011): Introducing financial frictions and unemployment into a small open economy model. Journal of Economic Dynamics and Control 35(12): pp. 1999-2041.
5) Smets, F. & R.Wouters (2003): An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of the European Economic Association 1(5): pp. 1123-1175.
|Popis:||The course lays out the basic New Keynesian dynamic stochastic general equilibrium (DSGE) model and introduces some of its major extensions: capital in production function, real rigidities in utility function and production sector, financial frictions in form of financial accelerator, and small-open economy adjustment. The core part of the course follows chapters 1-7 in Gali (2008) and the extensions follow mainly Smets & Wouters (2003), Bernanke et al. (1999), and Christiano et al. (2011).
Students will learn how to build, solve, simulate, and estimate the New Keynesian DSGE models, which are currently the most widespread structural models in macroeconomic literature and the main analytical tool of most central banks, including the Federal Reserve, the European Central Bank, and the Czech National Bank. Knowledge of these models is thus a necessity for any researcher and analyst concerned with macroeconomics.
Approximately half of the lecture time will be devoted to derivation of models and the other half to providing economic intuition and interpreting the model outcomes in light of empirical evidence. Seminars will additionally include also in-class exercises and sample solutions of problem sets. The course does not require any prior knowledge of DSGE models and it is appropriate for both the first-year and second-year M.A. students.
Students are expected to apply covered techniques throughout the course in the four problem sets (each 20% of final grade) and in the final written exam (20%), based mainly on problem sets.