59691 - Macroeconomics

Learning outcomes

By the end of the course, students will have developed a solid understanding of the macroeconomic models commonly used to analyse the determinants and dynamics of business cycles — the recurrent expansions and contractions of economic activity over time. In particular, students will gain familiarity with recursive methods in macroeconomics; real business cycles models; models incorporating nominal frictions (e.g., the New Keynesian paradigm); the role of monetary policy in stabilizing economic fluctuations; households' heterogeneity and their impact on macroeconomic outcomes; the role of expectations formation and information frictions.

Course contents

The course lies at the crossroad between quantitative methods and macroeconomics. Therefore, elementary concepts of linear algebra and calculus are required. Familiarity with MATLAB is desired.

By the end of the course, students will have developed a solid understanding of the macroeconomic models commonly used to analyze the determinants and dynamics of business cycles — the recurrent expansions and contractions of economic activity over time. The course organization more in details is as follows:

• The Real Business Cycle model, primitives, long-run equilibrium, linearization and solution;

• The New-Keynesian (NK) paradigm and models with nominal frictions;

• State space models and Kalman filter; the VAR representation of business cycle models;

• Departure from representative (RA) agent model: the Two Agent NK (TANK) model;

• Hands on business cycle models with Dynare.

Readings/Bibliography

Students can mostly rely on the slides. A list of suggested text books, readings and various sources follow:

Galì, J., Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework, Princeton University Press, 2008.

M. Azzimonti, P. Krusell, A. McKay, and T. Mukoyama, Macroecononomics, https://phdmacrobook.org/

Teaching methods

Theoretical classes will be supported and complemented by hands-on exercises. As a consequence, the course will require the use of computers and programs; we will work with Matlab and Dynare.

AI can be a useful tool to assist individual study with in-depth analysis, summaries and self-assessment paths.

Assessment methods

Assessment consists of one final exam. Grading scale:

<18 failed

18-23 sufficient

24-27 good

28-30 very good

30 e lode outstanding

The use of AI is prohibited during the exam. Any use is a violation of academic integrity.

Teaching tools

Slides, lecture notes, and readings will be available on the platform VIRTUALE: https://virtuale.unibo.it/

Office hours

See the website of Filippo Piermaria Ferroni