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Special Workshop on Macroeconomic Modeling and Simulation by Professor Junior Maih

The Center for Positive/Empirical Analysis of Political will hold a special workshop on “Macroeconomic Modeling and Simulation” by Professor Junior Maih, following last year’s workshop.



This is an introductory workshop to the modeling of macroeconomy. While the workshop is meant for graduate students and professionals, it does not require prior knowledge of the complexities involved in DSGE modeling and/or the RISE toolbox, which will be used for applications.


The intention of this introductory workshop is not to train participants to become specialists in the field, but instead for them to gain a basic exposure to the main techniques involved in solving and analyzing DSGE models so that they may start using those techniques in their work. Although no prior knowledge of DSGE modeling is assumed, the workshop is quite fast-paced and thus additional reading is provided to help participants to consolidate material. By the end of the workshop participants should:

– Understand the economic foundations of DSGE models and how they relate to policy issues.
– Be able to write dynamic models in a general form suitable for solution, simulation and estimation in a computer using the RISE toolbox.
– Implement basic solution and simulation techniques to analyze DSGE models, showing how the model economies respond to different shocks.


  • Lecturer: Prof. Junior Maih (Norges Bank, Norwegian Business School)
  • Date & Time: From October 18 to December 20, 2021 — Every Monday from 16:30 to 18:00 (JST)
  • Venue: Zoom
  • Open to Waseda graduate students and the general public, free admission
  • Registration: Please contact Prof.Hamano at [email protected]
  • System requirements (programs)
    Matlab (core matlab, optimization toolbox, statistics toolbox)
    MikTex, dropbox, oxedit (optional)

Schedule  (10 sessions, 90 minutes each)

Lecture 1: Constant-parameter DSGE modeling and Introduction to RISE (10/18)

A prototypical DSGE model
Solution strategies
The RISE programming environment
Implementation of the model
Deterministic solutions

Lecture 2: Solving the steady state (10/25)

Analytical solution: The steady state model
Analytical solution: The steady state file
Endogenous parameters
Numerical solutions: Equation simplification
Numerical solutions: alternative solvers
Numerical solutions: constraints on the search space
Numerical solutions: block triangularization
The balanced-growth path of nonstationary models

Lecture 3: Stochastic solution via first-order perturbation (11/1)

The dynamics
Basic usages of the solution

Lecture 4: Bayesian Estimation (11/8)

Implementation of priors
Measurement errors: importance and implementation
Posterior maximization
Posterior simulation
Illustrating uncertainty
Endogenous priors
Indirect inference

Lecture 5: Optimal policy (11/15)

Loose commitment

6. Lecture 6: Perturbation solutions of Regime Switching DSGE models with constant transition probabilities (11/22)

A prototypical model and the RISE implementation
Perturbation approaches
Multiplicity of steady states and their implications
Dynamic solution and mean-square stability
Independent Markov processes
Parameters controlled by several Markov processes

7. Lecture7: Regime Switching DSGE models with time-varying transition probabilities (11/29)

Endogenous probabilities in the prototypical model
Higher-order perturbation
Dynamic solution and heuristic stability
Computation of moments

8. Lecture 8: Bayesian Estimation of Regime Switching DSGE models (12/6)

Filtration and Likelihood computation in the presence of regime switching
State normalization and nonlinear constraints
Priors implementation
Nonlinear constraints
Posterior maximization and posterior simulation

9. Lecture 9: Further topics in Bayesian estimation (12/13)

Computation of Marginal data density
Convergence diagnostics
Model comparison

10. Lecture 10: Optimal Ramsey Policy under regime switching (12/20)

  • 1018







Wed, 06 Oct 2021

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