| Academic Year |
2024Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA220001 |
Subject Classification |
Specialized Education |
| Subject Name |
計量経済学 |
Subject Name
（Katakana） |
ケイリョウケイザイガク |
Subject Name in
English |
Econometrics |
| Instructor |
YAMADA HIROSHI |
Instructor
(Katakana) |
ヤマダ ヒロシ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Tues7-10：ENG 117 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
7/2: on-demand (possibly);
8/1: Face-to-face;
Others: Hybrid |
| 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 |
|
| 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) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.
Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. |
|---|
Class Objectives
/Class Outline |
Lecture of econometric theory |
| Class Schedule |
lesson1 Introduction to econometrics
lesson2 Matlab/GNU Octave
lesson3 Matrix algebra1 (partitioned matrix, determinant)
lesson4 Matrix algebra2 (eigenvalue, quadratic form)
lesson5 Estimation of regression coeficients1 (OLS)
lesson6 Estimation of regression coeficients2 (differentiation by vector, ROLS)
lesson7 Estimation of regression coeficients3 (projection, FWL theorem)
lesson8 Estimation of regression coeficients4 (R-squared)
lesson9 Properties of least-squares estimators1 (variance-covariance matrix of OLSE etc)
lesson10 Properties of least-squares estimators2 (Gauss-Markov theorem)
lesson11 Hypothesis testing1 (interval estimation)
lesson12 Hypothesis testing2 (t-test)
lesson13 Hypothesis testing3 (F-test)
lesson14 Miscellaneous matters (multicolinearity etc)
lesson15 Review |
Text/Reference
Books,etc. |
Handouts |
PC or AV used in
Class,etc. |
|
| (More Details) |
Projector |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Spend enough time on reading the course materials. |
| Requirements |
|
| Grading Method |
Final examination (about 90%), Home work (about 10%) |
| 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. |