2023/2024

Dynamic Stochastic General Equilibrium Models

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Mago-Lego

Delivered by: Department of Theoretical Economics

When: 3 module

Open to: students of one campus

Instructors: Arsenii Mishin

Language: English

ECTS credits: 3

Contact hours: 40

Course Syllabus

Abstract

This course continues the sequence of macroeconomics courses for Master's students. It aims to deepen our understanding of modern tools used in the analysis of macroeconomic processes and policies. A challenge facing policymakers is how to evaluate the net effects of forces operating on different parts of the economy. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. DSGE models have also become the standard workhorse models for the analysis of aggregate fluctuations. The primary focus of this course will be on the analysis, solution, calibration, estimation, and extension of DSGE models. We will also emphasize computational methods and apply them to solve DSGE models.

Learning Objectives

• To deepen understanding of modern tools for the analysis of macroeconomic processes.
• To increase competitiveness of students in doing research where demand is rising for more rigorous work on studying macroeconomic processes.
• To learn and practice quantitative methods which are actively used in modern macro research.

Expected Learning Outcomes

• Critically evaluate the logic of DSGE modeling
• Apply computer programs to the work with DSGE models.
• Derive model's equations, compute the solution, perform model's calibration and estimation.
• Learn how to introduce different changes into the basic RBC model and evaluate their effects on the main results.
• Learn the theory and practice of parameter estimation in DSGE models using several examples and Dynare's estimation toolbox.
• Learn some basic tools and apply them to the work with non-linear models.
• Differentiate DSGE modeling with other approaches to macroeconomic modeling.
• Apply artificial intelligence tools to the work with DSGE models

Course Contents

• Overview of DSGE Models
• Introduction to MATLAB and Dynare
• The Methodology of DSGE Analysis: RBC Example
• Extensions of the Basic RBC Model
• Estimation of DSGE Models
• Introduction to Non-linear Models

Assessment Elements

• Home Assignments
• Class Participation
• Quizzes
• Presentation
  The list of papers will be given. Each student will choose one paper on a first come, first served basis and present it. Paper presentations will be 12-15 minutes in length.
• Final Test

Interim Assessment

• 2023/2024 3rd module
  0.1 * Class Participation + 0.35 * Final Test + 0.25 * Home Assignments + 0.15 * Presentation + 0.15 * Quizzes

Bibliography

Recommended Core Bibliography

• Dynamic macroeconomics, Alogoskoufis, G., 2019
• Macroeconomic theory : a dynamic general equilibrium approach, Wickens, M., 2008
• Methods for applied macroeconomic research, Canova, F., 2007
• Structural macroeconometrics, DeJong, D. N., 2007
• The ABCs of RBCs : an introduction to dynamic macroeconomic models, McCandless, G., 2008
• Wickens, M. (2008). Macroeconomic Theory : A Dynamic General Equilibrium Approach. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=350058

Recommended Additional Bibliography

• Bayesian estimation of DSGE models, Herbst, E. P., 2016
• Kenneth L. Judd. (1998). Numerical Methods in Economics. The MIT Press.
• Ljungqvist, L., & Sargent, T. J. (2012). Recursive Macroeconomic Theory (Vol. 3rd ed). Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=550665
• Numerical methods in economics, Judd, K. L., 1998

Authors

• Mishin Arsenii Olegovich