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2020　Econometrics I

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Ogasawara Kota 
Class Format
Lecture    (ZOOM)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W934)  Fri5-6(W934)  
Group
-
Course number
IEE.B207
Credits
2
Academic year
2020
Offered quarter
3Q
Syllabus updated
2020/9/29
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course is designed for 3rd or 4th year undergraduate students and is taught in Japanese. English language is used for the blackboarding in the preparation for Advanced Econometrics (IEE.B 405). Note that some students audit both Econometrics II and Advanced Econometrics in this quarter.

Student learning outcomes

The course aims to present and illustrate the theory and techniques of modern econometric analysis.

Keywords

Least square estimation, normal regression model, maximum likelihood estimation, nonlinear models, endogeneity

Competencies that will be developed

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

Class flow

The first part begins with concepts of the conditional expectation. The second part introduces concepts of the least square regression. The third part examines concepts of the normal regression model, maximum likelihood estimator, and a few nonlinear models. The final part introduces concepts of endogeneity.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Orientation and introduction Orientation and introduction
Class 2 Basic concepts I: Conditional expectation function Basic concepts I: Conditional expectation function
Class 3 Basic concepts II: Properties of the conditional expectation Basic concepts II: Properties of the conditional expectation
Class 4 The linear projection model The linear projection model
Class 5 The algebra of least squares I: Least squares estimator The algebra of least squares I: Least squares estimator
Class 6 The algebra of least squares II: Least squares estimator The algebra of least squares II: Least squares estimator
Class 7 The algebra of least squares III: FWL theorem The algebra of least squares III: FWL theorem
Class 8 Finite-sample properties of the OLSE Finite-sample properties of the OLSE
Class 9 Normal regression model and MLE Normal regression model and MLE
Class 10 Endogeneity I: Concept of causal effect Endogeneity I: Concept of causal effect
Class 11 Endogeneity II: Two-stage least squares Endogeneity II: Two-stage least squares
Class 12 Empirical examples Empirical examples
Class 13 Review Review
Class 14 Exercise Exercise

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Bruce E. Hansen. Econometrics. University of Wisconsin, 2020 (Chapters 1--5).

Reference books, course materials, etc.

A recommended supplementary monograph is Mastering Metrics by Joshua D. Angrist & Jorn-Steffen Pischle.

Assessment criteria and methods

Problem solving or midterm 30%, final exams 70%.

Related courses

  • IEE.A205 ： Statistics for Industrial Engineering and Economics
  • IEE.A204 ： Probability for Industrial Engineering and Economics
  • IEE.B301 ： Econometrics II
  • IEE.B405 ： Advanced Econometrics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

I recommend Introductory Courses in Statistics and Probability (level: IEE 200) by Professor Masami Miyakawa as the prerequisites. Students should be familiar with basic concepts in probability and statistical inference. Familiarity with matrix algebra is preferred.

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