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B1016 - RECURSIVE METHODS IN MACROECONOMICS

Academic Year 2022/2023

                
                        Docente:
                        Giulio Fella
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-P/01
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Economics (cod. 8408)

                                    Course Timetable
                                
from Feb 13, 2023 to Mar 22, 2023

Learning outcomes

The course provides students with the fundamental training required to pursue independent research in modern macroeconomics.

It covers recursive methods---Markov processes, numerical dynamic programming, and recursive equilibria---and their application to core dynamic stochastic models: business cycle and heterogeneous agents models.

Course contents

0. Roadmap: recursive methods in economics

I. Markov processes

Markov chains
VARs
General framework for Markov processes

II. Dynamic programming

Deterministic problem
Stochastic problem
Numerical dynamic programming I: approximating the state space
General results for dynamic optimisation
Linear-quadratic problem
The optimal linear regulator problem
Numerical dynamic programming II: approximating functional forms

III. Recursive competitive equilibria

Equilibrium with Arrow securities
Using the planning problem
Heterogeneous agent problems

Readings/Bibliography

The main textbook is Ljungqvist, L and Sargent, T J, Recursive Macroeconomic Theory, 2nd edition, MIT Press, 2004 (henceforth LjSa)

Additional references will provided during the course.

Teaching methods

Lectures and Classes.

Assessment methods

Four homeworks involving computation – 20% (each worth 5%)
Final exam (2 hours) – 80%

Marks: fail <18; pass 18-23; good 24-27; very good 28-30; outstanding 30 cum laude

Teaching tools

Lecture slides and other material made available using the unibo online platform.

Office hours

See the website of Giulio Fella