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- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Higuchi Yoichiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6(W934) Fri5-6(W934)
- Group
- -
- Course number
- IEE.B207
- Credits
- 2
- Academic year
- 2017
- Offered quarter
- 3Q
- Syllabus updated
- 2017/9/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
Econometrics is a tool for proving hypotheses deduced from economic theories. Estimation can be done very simply by least squares method, and hypotheses can thus be tested. However, this simplicity often hides presumptions which guarantee credibility of the test results. Posterior evaluation to examine effectiveness of the presumptions is demanded. If some presumptions are found to be failed, more sophisticated estimation methods and testing methods should be applied.
Econometrics is also a tool for forecasting. However, in the forecasting process, the fact that estimated parameters and predicted values are stochastic variables is often set aside. We should learn that variance of the forecast is equally important to its value.
Student learning outcomes
Starting from the Classical Simple Regression model, we study multiple regression model with matrix algebra, and methods to test hypothesis either simple or combined for examining structural changes. Further we study how to deal with problems caused by model specification, i.e. selection of explanatory variables and functional form, and those by multiple collinearity.
Regression models are then extended as generalized classical regression model to treat with problems caused by heteroskedasticity and serial correlation.
Finally Neoclassical regression models are studied to handle stochastic explanatory variables, with both instrumental variable method and generalized moment method being employed.
Keywords
Least Sqaures Method, Regression Model, Classical Regression Model, Matrix Algebra, Hypothesis Testing,Structural Change, Model Specification, Multiple-collinearity, Generalized Classical Regression Model, Heteroskedasticity, Serial Correlation, Neoclassical Regression Model, Stochastic Explanatory Variable, Generalized Moment Method.
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
At the beginning, solutions to some exercises related to the preceding class are explained. Scheduled contents are then lectured on. Exercises of the related chapter are given as homework.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Conditional Expected Value and Line Fitting | End-of-chapter exercise related to scheduled contents of the class |
| Class 2 | Classical Bivariate Regression Model | |
| Class 3 | Test of Parameters and Forecast | |
| Class 4 | Multivariate Regression Model | |
| Class 5 | Matrix Algebra for Linear Regression | |
| Class 6 | Classical Multivariate Regression Model | |
| Class 7 | Hypothesis Test | |
| Class 8 | Application of Multivariate Regression Model | |
| Class 9 | Test of Multiple Hypotheses: Constrained Regression and Test of Structural Change | |
| Class 10 | Model Specification | |
| Class 11 | Multi-Collinearity | |
| Class 12 | Generalized Classical Regression Model: Heteroskedasticity and Serial Correlation | |
| Class 13 | Generalized Classical Regression Model: Estimation of Simultaneous Equations | |
| Class 14 | Neoclassical Regression Model: Stochastic Explanatory Variable and Probability Limit | |
| Class 15 | Neoclassical Regression Model: Instrumental Variable and Generalized Moment Method |
Textbook(s)
Asano & Nakamura(2009) "Econometrics" Second Edition, Yuuhikaku (in Japanese)
Reference books, course materials, etc.
No particular references.
Assessment criteria and methods
40％ by Short tests and homeworks, and 60% by Final Examination.
Related courses
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.B301 ： Econometrics II
- IEE.B336 ： Applied Econometrics
- IEE.B405 ： Advanced Econometrics
- IEE.B434 ： Advanced Topics in Econometrics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Prerequisite
IEE.A204 ： Probability for Industrial Engineering and Economics
IEE.A205 ： Statistics for Industrial Engineering and Economics
