An Advanced Course on the Science and Art of DSGE Modelling
The Essentials course covers basic building blocks of numerical analysis, their use in solving modern macroeconomic models and an introduction into model estimation. Participants use Matlab code to solve and analyse macroeconomic models.
Each day consists of lecture sessions and exercise sessions. Slides and codes are provided below.
Day 1:
- Slides (Joo Nunes Park)
- Advanced Course Book 2021
- Code
- ReadMe for Participants
Day 2
Articles
- Consumption Inequality and Partial Insurance
- Insurance and opportunities: A welfare analysis of labor market risk
- How should tax progressivity respond to rising income inequality?
- How much Consumption Insurance Beyond Self-Insurance?
Slides
- Insurance and Opportunities: A Welfare Analysis of Labor Market Risk
- How Should Tax Progressivity Respond to Rising Income Inequality?
- Introduction to Heterogeneous Agents and Risk Sharing
- Consumption Insurance
- A Life-cycle Bewley Economy
Day 3: Parameterized Expectations Algorithm and Blanchard-Kahn conditions (Slides)
- Parameterized expectations algorithm
- Value function iteration
- Accuracy tests: Euler errors, dynamic Euler equation test, DHM statistic
- Occasionally binding constraints and penalty functions
- Blanchard-Kahn conditions
- Sun spots and self-fulfilling expectations
- Exercises (Code)
- Additional notes (Notes)
Day 4: Kalman filter & full information methods (Slides)
- Kalman filter
- State space form
- Maximum Likelihood
- Avoiding the singularity problem
- Exercises (Code)
- Additional notes (Notes)
Day 5: Bayesian estimation (Slides)
- Bayesian estimation
- Markov Chain Monte Carlo methods
- Metropolis and Metropolis Hastings
- Maximum Likelihood, GMM, SMM, idea of calibration
- Exercises (Code)