2019 Econometrics I

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Academic unit or major
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
Academic year
2019
Offered quarter
3Q
Syllabus updated
2019/9/19
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.

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

Competencies that will be developed

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

Class flow

Scheduled contents are lectured on. Answers to end-of-chapter exercises are given in advance, and students scrutinize them as homework.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction
Class 2 Conditional Expected Value and Line Fitting
Class 3 Classical Bivariate Regression Model End-of-chapter exercise related to scheduled contents of the class
Class 4 Test of Parameters and Forecast
Class 5 Multivariate Regression Model End-of-chapter exercise related to scheduled contents of the class
Class 6 Classical Multivariate Regression Model
Class 7 Hypothesis Test End-of-chapter exercise related to scheduled contents of the class
Class 8 Application of Multivariate Regression Model
Class 9 Test of Multiple Hypotheses: Constrained Regression and Test of Structural Change End-of-chapter exercise related to scheduled contents of the class
Class 10 Model Specification
Class 11 Multi-Collinearity End-of-chapter exercise related to scheduled contents of the class
Class 12 Generalized Classical Regression Model: Heteroskedasticity and Serial Correlation (1)
Class 13 Generalized Classical Regression Model: Heteroskedasticity and Serial Correlation (2) End-of-chapter exercise related to scheduled contents of the class
Class 14 Generalized Classical Regression Model: Estimation of Simultaneous Equations (1)
Class 15 Generalized Classical Regression Model: Estimation of Simultaneous Equations (2) End-of-chapter exercise related to scheduled contents of the class

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 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

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