G6216443 International Economic Policy 2

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Economics Economics Day Course

Lecture Code G6216443 Subject Classification Specialized Education

Subject Name 国際経済政策論２

Subject Name
（Katakana）コクサイケイザイセイサクロン２

Subject Name in
English International Economic Policy 2

Instructor YASUTAKE KOUICHI

Instructor
(Katakana) ヤスタケ　コウイチ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Weds1-4：ECON B159

Lesson Style Lecture Lesson Style
(More Details) 　

The class will be a mix of lectures, exercises, discussions, and student presentations.The class may be conducted online due to the teacher's travel, but this will be announced in advance.

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students

Keywords Machine Learning, Deep Learning, Foreign Exchange Rate, Time Series Analysis, International Economic Variables

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) Economic Analysis
（Abilities and Skills）
・The ability to analyze and examine political issues applying knowledge on economic policy, international economics and economic matters and so on.

Class Objectives
/Class Outline This course aims to master the following skills and knowledge by learning the analysis of time series data of international economic variables through cooperative hands-on activities.

- Master the fundamentals of time series analysis using traditional statistical models.
- Master the fundamentals of data science time series analysis using artificial intelligence approaches.
- You will learn clearly that the traditional statistical model and the data science approach are completely different.
- Students will master basic Unix operations and become more familiar with computers through hands-on data analysis and management (no prior knowledge or skills are required).
- Students will learn the basics of academic writing by writing a group report on the forecasting of economic variables.

Class Schedule lesson1 Guidance (contents, goals, style, prerequisite basic knowledge, grading, etc. of this class)
lesson2 Introduction to Unix and Python (no prerequisite basic knowledge required)
lesson3 Fundamentals and Forecasting of Time Series Data
lesson4 Forecasting using traditional statistical models (1) ARMA and ARIMA processes (stationary and non-stationary) (Issue Report) Can we say that GAFA stock prices are on a random walk?
lesson5 Forecasting using traditional statistical models (2) SARIMA process and forecasting
lesson6 Forecasting using traditional statistical models (3) (Try it out (assignment report)) Forecasting international airline passenger volume, real GDP and international comparison
lesson7 Forecasting using traditional statistical models (4) VAR process
lesson8 Forecasting using traditional statistical models (5) (Try it out (assignment report)) Forecasting real disposable and expendable income and international comparisons
lesson9 Data Science Approach (1) What is Deep Learning? (Basic knowledge and implementation of elementary DNN models)
lesson10 Data Science Approach (2) Implementation of LSTM Architecture
lesson11 Data Science Approach (3) Implementation of CNN Architecture and Combination with LSTM
lesson12 Data Science Approach (4) ARLSTM Architecture Implementation
lesson13 Data Science Approach (5) (Try it out (Issue Report)) Electricity Consumption Forecasting and International Comparison
lesson14 Data Science Approach (6) (Try it out (Assignment Report)) Forecasting and Comparative Analysis of Foreign Exchange Rates
lesson15 (Trial Report) Prepare a report on time series data analysis of international economic variables using either a traditional statistical approach or a data science approach.

Although the syllabus is full of katakana, no basic knowledge of programming languages or Unix is required. Even if you are a true beginner, we will guide you responsibly. Please do not worry. In addition to the final final report, we plan to make group reports for all the assignments in class. We will guide you on how to prepare group reports in class, so please do not worry about this point as well.

Text/Reference
Books,etc. Documents available on GitHub will be instructed in advance as textbooks and reference books.

PC or AV used in
Class,etc. 　

(More Details) We will actively use materials, notes, documents, etc. available on GitHub, and plan to introduce ChatGPT to the class.

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review No basic knowledge of computers or finance is assumed. Don't worry, even if you don't know how to manipulate a Windows file! Don't worry. If you do your homework in class, or if you do your homework before and after class, you will make friends with computers. Come to class with a dream in mind.

Requirements Please bring your laptop computer (Note PC) to every class. (You can't do anything with only a smartphone to begin with.)

Grading Method Grades are based on class participation, contribution to group activities, assignment reports, and final reports.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message The class is a data analysis class, so we will inevitably use computers a lot, but we do not ask what your current level of knowledge or skill is. What is important is whether or not you have dreams and hopes for data analysis and data science. If you do, you do not need to know anything about programming. We will guide you one by one with responsibility. We encourage you to take on this challenge.

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.

Academic Year	2024Year	School/Graduate School	School of Economics Economics Day Course
Lecture Code	G6216443	Subject Classification	Specialized Education
Subject Name	国際経済政策論２
Subject Name （Katakana）	コクサイケイザイセイサクロン２
Subject Name in English	International Economic Policy 2
Instructor	YASUTAKE KOUICHI
Instructor (Katakana)	ヤスタケ　コウイチ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Weds1-4：ECON B159
Lesson Style	Lecture	Lesson Style (More Details)
The class will be a mix of lectures, exercises, discussions, and student presentations.The class may be conducted online due to the teacher's travel, but this will be announced in advance.
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students
Keywords	Machine Learning, Deep Learning, Foreign Exchange Rate, Time Series Analysis, International Economic Variables
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)	Economic Analysis （Abilities and Skills）・The ability to analyze and examine political issues applying knowledge on economic policy, international economics and economic matters and so on.
Class Objectives /Class Outline	This course aims to master the following skills and knowledge by learning the analysis of time series data of international economic variables through cooperative hands-on activities. - Master the fundamentals of time series analysis using traditional statistical models. - Master the fundamentals of data science time series analysis using artificial intelligence approaches. - You will learn clearly that the traditional statistical model and the data science approach are completely different. - Students will master basic Unix operations and become more familiar with computers through hands-on data analysis and management (no prior knowledge or skills are required). - Students will learn the basics of academic writing by writing a group report on the forecasting of economic variables.
Class Schedule	lesson1 Guidance (contents, goals, style, prerequisite basic knowledge, grading, etc. of this class) lesson2 Introduction to Unix and Python (no prerequisite basic knowledge required) lesson3 Fundamentals and Forecasting of Time Series Data lesson4 Forecasting using traditional statistical models (1) ARMA and ARIMA processes (stationary and non-stationary) (Issue Report) Can we say that GAFA stock prices are on a random walk? lesson5 Forecasting using traditional statistical models (2) SARIMA process and forecasting lesson6 Forecasting using traditional statistical models (3) (Try it out (assignment report)) Forecasting international airline passenger volume, real GDP and international comparison lesson7 Forecasting using traditional statistical models (4) VAR process lesson8 Forecasting using traditional statistical models (5) (Try it out (assignment report)) Forecasting real disposable and expendable income and international comparisons lesson9 Data Science Approach (1) What is Deep Learning? (Basic knowledge and implementation of elementary DNN models) lesson10 Data Science Approach (2) Implementation of LSTM Architecture lesson11 Data Science Approach (3) Implementation of CNN Architecture and Combination with LSTM lesson12 Data Science Approach (4) ARLSTM Architecture Implementation lesson13 Data Science Approach (5) (Try it out (Issue Report)) Electricity Consumption Forecasting and International Comparison lesson14 Data Science Approach (6) (Try it out (Assignment Report)) Forecasting and Comparative Analysis of Foreign Exchange Rates lesson15 (Trial Report) Prepare a report on time series data analysis of international economic variables using either a traditional statistical approach or a data science approach. Although the syllabus is full of katakana, no basic knowledge of programming languages or Unix is required. Even if you are a true beginner, we will guide you responsibly. Please do not worry. In addition to the final final report, we plan to make group reports for all the assignments in class. We will guide you on how to prepare group reports in class, so please do not worry about this point as well.
Text/Reference Books,etc.	Documents available on GitHub will be instructed in advance as textbooks and reference books.
PC or AV used in Class,etc.
(More Details)	We will actively use materials, notes, documents, etc. available on GitHub, and plan to introduce ChatGPT to the class.
Learning techniques to be incorporated
Suggestions on Preparation and Review	No basic knowledge of computers or finance is assumed. Don't worry, even if you don't know how to manipulate a Windows file! Don't worry. If you do your homework in class, or if you do your homework before and after class, you will make friends with computers. Come to class with a dream in mind.
Requirements	Please bring your laptop computer (Note PC) to every class. (You can't do anything with only a smartphone to begin with.)
Grading Method	Grades are based on class participation, contribution to group activities, assignment reports, and final reports.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	The class is a data analysis class, so we will inevitably use computers a lot, but we do not ask what your current level of knowledge or skill is. What is important is whether or not you have dreams and hopes for data analysis and data science. If you do, you do not need to know anything about programming. We will guide you one by one with responsibility. We encourage you to take on this challenge.
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.

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