### 2016　Econometrics I

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Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Higuchi Yoichiro
Course component(s)
Lecture
Day/Period(Room No.)
Tue5-6(W934)  Fri5-6(W934)
Group
-
Course number
IEE.B207
Credits
2
2016
Offered quarter
3Q
Syllabus updated
2017/1/11
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

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
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### Class flow

At the beginning, solutions to 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 