| Academic Year |
2024Year |
School/Graduate School |
School of Economics Economics Day Course |
| Lecture Code |
G5051121 |
Subject Classification |
Specialized Education |
| Subject Name |
計量経済学１ |
Subject Name
（Katakana） |
ケイリヨウケイザイガクイチ |
Subject Name in
English |
Econometrics 1 |
| Instructor |
HAYAKAWA KAZUHIKO |
Instructor
(Katakana) |
ハヤカワ カズヒコ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
2nd-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Weds1-4：ECON B257 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Basically, Face-to-Face lectures are provided. However, it may switch to online lecture depending on the circumstances. |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
2
:
Undergraduate Low-Intermediate
|
| Course Area（Area） |
24
:
Social Sciences |
| Course Area（Discipline） |
03
:
Economics |
| Eligible Students |
second year |
| Keywords |
statistical analysis of economic data, regression analysis, empirical analysis |
| 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 of developing mathematical analysis applying knowledge Economic theory, Statistics and Econometrics. |
|---|
Class Objectives
/Class Outline |
Econometrics is an interdisciplinary subject composed of economics, statistics and mathematics. In this course, the statistics part is mainly considered. Specifically, this course provide a series of lectures associated with regression analysis. The goal of this course is to provide basic skills so that students can carry out simple statistical analysis. |
| Class Schedule |
lesson1: Guidance
lesson2: What is econometrics?
lesson3: Review of statistics
lesson4: Review of statistics
lesson5: Review of statistics
lesson6: Simple regression model
lesson7: Simple regression model
lesson8: Simple regression model
lesson9: Multiple regression model
lesson10: Multiple regression model
lesson11: Multiple regression model
lesson12: Functional form
lesson13: Dummy variable
lesson14: Dummy variable
lesson15: Summary of the course
class room report, final exam |
Text/Reference
Books,etc. |
distribute the materials via webpage |
PC or AV used in
Class,etc. |
|
| (More Details) |
slides |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Every classes are interdependent, so students are required to review each class. |
| Requirements |
Basic knowledge of statistics and mathematics |
| Grading Method |
HW and final exam |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
Students who are interested in data analysis are most welcome. |
| 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. |