The Macroeconomics course has been organized in two parts. The first part contains an introductory lesson on the history of macroeconomics and lessons on economic growth theory, real business cycle and New Keynesian models. The second part illustrates advanced theoretical models on monetary policy, network analysis and agent-based model. Matlab tools for macroeconomic models are introduced to support the two parts in terms of empirical applications.
Learning outcomes:
Prerequisites:
Students should have knowledge of the following topics:
Textbooks:
MACROECONOMICS – PART 1
Syllabus
ꙩHistory of Macroeconomics
Prof. Mauro Gallegati
Hours: 3
Reading list:
De Vroey, M.A., History of Macroeconomics from Keynes to Lucas and Beyond, Cambridge University Press, 2016.
Gallegati, M., Il mercato rende liberi – E altre bugie del neoliberismo, Luiss University Press, 2021.
ꙩ Economic Growth Theory
Prof. Giulia Bettin
Prof. Davide Ticchi
Hours: 16
Reading list:
Further readings:
ꙩ Real Business Cycle
Dr. Federico Giri
Hours: 12
Reading list:
ꙩ New Keynesian Model
Dr. Federico Giri
Hours: 12
ꙩ Matlab tools for Macroecnomics
Prof. Antonio Palestrini
Hours: 6
Reading list:
MACROECONOMICS – PART 2
Syllabus
ꙩ Monetary Policy
Prof. Paolo Canofari
Prof. Alessandro Piergallini
Hours: 12
Reading list:
ꙩ Network Analysis
Hours: 8
Reading list:
Jackson M.O. (2008) Social and Economic Networks, Princeton: Princeton University Press.
Easley D. and Kleinberg J. (2010) Networks, Crowds, and Markets, Cambridge: Cambridge University Press.
ꙩ Agent-Based Model
Prof. Eugenio Caverzasi
Dr. Samantha Coccia
Dr. Edoardo Luca Fierro
Prof. Antonio Palestrini
Prof. Alberto Russo
Hours: 39
Reading list:
Exam
At the end of the first part of Macroeconomics course, doctoral students take an exam for each of the three courses taught i.e., Economic Growth Theory, Real Business Cycle, and New Keynesian Models. The same indications are adopted for the second part of Macroeconomics course, where the three courses taught are: Monetary Policy, Network Analysis, and Agent-based Model. Overall, there are 6 exams, consisting in an open question and an exercise for each course. The exam is passed if the PhD student scores 18 out of 30. The duration of the exam for each course is 1 hour and 30 minutes.
E-learning: