| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Economics Program |
| Lecture Code |
WMEB2200 |
Subject Classification |
Specialized Education |
| Subject Name |
マクロ経済分析 |
Subject Name
（Katakana） |
マクロケイザイブンセキ |
Subject Name in
English |
Macroeconomic Analysis |
| Instructor |
MIYAZAKI KOICHI |
Instructor
(Katakana) |
ミヤザキ コウイチ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Tues1-4：ECON A206 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| Lecture |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
E
:
English |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
24
:
Social Sciences |
| Course Area（Discipline） |
03
:
Economics |
| Eligible Students |
First and second year student |
| Keywords |
Optimization, Dynamic programming |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
In this course, we will learn dynamic programming, a useful method for solving dynamic models. Ultimately, we will cover examples of macroeconomic analysis using this method and numerical computation techniques. |
| Class Schedule |
Lesson 1：Guidance
Lesson 2：Solow Growth Model and Dynamic Programming
Lesson 3：Mathematical Preparation for Dynamic Programming: Metric Spaces, Functions, and Sets
Lesson 4：Mathematical Preparation for Dynamic Programming: Contraction Mapping Theorem and Correspondences
Lesson 5：Principle of Optimality (Bounded Returns): From Sequential Problems to Dynamic Programming
Lesson 6：Principle of Optimality (Bounded Returns): From Dynamic Programming to Sequential Problems
Lesson 7：Principle of Optimality (Unbounded Returns)
Lesson 8：Numerical Computation Methods
Lesson 9：Behavior of Solutions in Dynamic Models
Lesson 10：Behavior of Solutions in Dynamic Models: Stability
Lesson 11：Dynamic Programming under Uncertainty
Lesson 12：Markov Processes
Lesson 13：Applications of Dynamic Programming (1)
Lesson 14：Applications of Dynamic Programming (2)
Lesson 15：Summary of the course
A problem set will be assigned regularly.
This schedule is always subject to change. |
Text/Reference
Books,etc. |
A textbook and references will be announced in the first class. |
PC or AV used in
Class,etc. |
Handouts, Microsoft Teams, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Each class relates to each other. Hence, resolving what you do not understand in the class before the next class is a good strategy for the success in this course. |
| Requirements |
None. |
| Grading Method |
Based on your performance of problem sets (50%) and in class (50%), a grade will be given. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |