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2024　Advanced Topics in Macroeconomics

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Academic unit or major
Graduate major in Industrial Engineering and Economics
Instructor(s)
Morita Hiroshi 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
IEE.B432
Credits
2
Academic year
2024
Offered quarter
3Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

Students study the method of macroeonometrics that can bridge the data and theory related with macroeconomics. The object of this course is to understand the structure of Dynamic Stochastic General Equilibrium model and to implement empirical analyses of using macroeonomic data.

Student learning outcomes

The goals of this course are as follows.
1. Students can build the Dynamic Stochastic General Equilibrium General model.
2. Students can implement macroeonometric analysis based on the macroeconomic data and theory.
3. Students can interpret economically the results derived from their analysis.

Keywords

Dynamic Stochastic General Equilibrium model, Vector autoregressiv model, Bayesian estimation

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Lecture-style class will be conducted using slides and board. I will use the Matlab software in several times.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Guidance Explain macroeconometrics.
Class 2 RBC model 1: Structure Explain the structure of RBC model.
Class 3 RBC model 2: Phase diagram Explain the structure of RBC model using phase diangram.
Class 4 RBC model 3: Linear approximation Explain the method of Linear approximation.
Class 5 NK model 1: Structure Introduce the structure of NK model.
Class 6 NK model 2: Derivation of NKPC Derive the NKPC.
Class 7 NK model 3: Dynare Explain the simulation analysis using Dynare.
Class 8 Bayesian Estimation Explain the Bayesian estimation method.
Class 9 VAR model and Gibbs sampler Explain the structure of VAR model and its estimaton method using Gibbs sampler.
Class 10 Marcov-switching model Explain Marcov-switching model and its application.
Class 11 Smooth transition model and MH algorithm Explain smooth transition model and its estimation method using MH algorithm.
Class 12 State-space model Explain the Bayesian estimation method of state-space model.
Class 13 Structural estimation of dynamic macroeconomic model Explain the Bayesian estimation of NK model using state-space model.
Class 14 Summary Summary

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to the course materials.

Textbook(s)

No textbook for this course.

Reference books, course materials, etc.

・Kim, C-J, and Charles R. Nelson, 1999. State-Space Models With Regime Switching: Classical and Gibbs-Sampling approaches With Applications, MIT Press.
・McCandless, G., 2008. The ABCs of RBCs - An Introduction to Dynamic Macroeconomic Models-, Harvard University Press.
・Joshua Chan, Gary Koop, Dale J. Poter and Justin L. Tobias, 2019. Bayesian Econometric Methods, Cambridge University Press.

Assessment criteria and methods

Final exams (60%) and Report (40%)

Related courses

  • IEE.B332 ： Applied Macroeconomics
  • IEE.B402 ： Advanced Macroeconomics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are supposed to have graduate school-level knowledge of macroeconomics, microeconomics, and econometrics.

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