Dynamic Stochastic General Equilibrium (DSGE) Modeling
This comprehensive online course is designed to cater to different levels of expertise in DSGE modeling, providing a structured path from introductory concepts to advanced applications. The course is targeted at academics, PhD students, and professors who wish to develop and refine their skills in DSGE modeling for economic analysis and policy applications.
Levels:
Introductory Level
Intermediate Level
Advanced Level
Each level consists of modules that include video lectures, readings, hands-on exercises, assignments, and projects. The course will utilize software tools such as MATLAB, Dynare, and R for practical implementation.
Course Objectives:
Understand the basic concepts and structure of DSGE models.
Learn the process of building and solving simple DSGE models.
Get familiar with the computational tools used in DSGE modeling.
Target Audience:
Academics and PhD students new to DSGE modeling.
Professors looking to introduce DSGE modeling in their courses.
Course Structure: Module 1: Introduction to DSGE Models
Week 1: Overview of DSGE models: history and applications.
Week 2: Key components: agents, markets, and shocks.
Module 2: Building Basic DSGE Models
Week 3: Setting up a simple DSGE model: representative agent and preferences.
Week 4: Production, technology, and market structures.
Module 3: Solving DSGE Models
Week 5: Linearization techniques and the role of steady states.
Week 6: Introduction to computational tools: MATLAB and Dynare.
Readings:
"Recursive Macroeconomic Theory" by Lars Ljungqvist and Thomas J. Sargent.
"Macroeconomic Theory: A Dynamic General Equilibrium Approach" by Michael Wickens.
Assignments:
Weekly problem sets involving model setup and solution.
A mini-project to construct and solve a basic DSGE model.
Final Project:
Build and solve a simple DSGE model using Dynare.
Course Objectives:
Develop more complex DSGE models incorporating various economic sectors.
Learn estimation techniques and calibration methods.
Analyze policy implications using DSGE models.
Target Audience:
Academics, PhD students, and professors with a basic understanding of DSGE models.
Course Structure: Module 1: Advanced DSGE Model Structures
Week 1: Incorporating capital and investment.
Week 2: Introducing government sector and fiscal policy.
Module 2: Estimation and Calibration
Week 3: Calibration techniques: parameter selection and data fitting.
Week 4: Estimation methods: Bayesian estimation and Maximum Likelihood.
Module 3: Policy Analysis and Shocks
Week 5: Analyzing monetary policy: Taylor rules and interest rate dynamics.
Week 6: Understanding and modeling different types of shocks (technology, policy, preference).
Readings:
"Dynamic Economic Analysis: Deterministic Models in Discrete Time" by Gerhard Sorger.
Selected articles from the Journal of Economic Dynamics and Control.
Assignments:
Weekly assignments on model expansion and policy analysis.
A mid-term project on estimating a DSGE model using real data.
Final Project:
Develop a medium-scale DSGE model and perform policy analysis using Bayesian estimation.
Course Objectives:
Master advanced DSGE modeling techniques and applications.
Apply DSGE models to current economic research questions.
Contribute to academic research and policy-making using advanced DSGE models.
Target Audience:
Experienced academics, advanced PhD students, and professors specializing in macroeconomics.
Course Structure: Module 1: State-of-the-Art DSGE Modeling Techniques
Week 1: Nonlinear DSGE models and solution techniques.
Week 2: Incorporating heterogeneous agents and incomplete markets.
Module 2: Advanced Computational Methods
Week 3: High-dimensional models and computational challenges.
Week 4: Using advanced tools: MATLAB toolboxes, R, and Python.
Module 3: DSGE Models in Frontier Research
Week 5: DSGE models in climate economics and macro-finance.
Week 6: DSGE models for emerging market economies and developing countries.
Readings:
"Advanced Macroeconomics" by David Romer.
Selected cutting-edge research papers from top economic journals.
Assignments:
Weekly assignments on advanced model development and computational techniques.
A mid-term project involving the development of a novel DSGE model.
Final Project:
Conduct original research using advanced DSGE models, culminating in a research paper suitable for academic publication.
Certification:
Participants who complete all modules, assignments, and the final project at each level will receive a certificate of completion for that level.
A comprehensive certificate will be awarded upon completion of all three levels.
Course Delivery:
The course will be delivered through a combination of pre-recorded video lectures, live Q&A sessions, interactive coding tutorials, and discussion forums.
All course materials, including readings, software guides, and lecture slides, will be available online.
Instructor Team:
The course will be led by a team of experienced economists and econometricians with extensive expertise in DSGE modeling.
Enrollment:
Participants can enroll through the university’s online learning platform.
Enrollment will be open to individuals with a background in macroeconomics and basic econometrics.
By the end of this course, participants will have a thorough understanding of DSGE modeling techniques and be equipped with the skills to apply these models to economic research, policy analysis, and academic publication.
Funding opportunities:
The following concessions is available: 20% discount for participants working or studying in institutions in developing countries.