前期 水曜日 2講時 経済学研究科401演習室. 単位数/Credit(s): 2. 担当教員/Instructor: KO IAT MENG. 対象学年/Eligible Participants: 3・4. 履修年度: 2024. 科目ナンバリング/Course Numbering: EAL-ECO366E. 使用言語/Language Used in Course: English.
Advanced Econometrics(Special Lectures)I
KO IAT MENG
This course is one-semester advanced level econometrics. The prerequisites are Econometrics I and II. This course should be regarded as the entry level econometrics course for PhD program. Students are required to complete the two courses before enrolling in this one. In this course, we will study econometrics in greater depth with both old topics (such as linear regression) and new topics (such as panel data model). Mathematical tools, for example, vector and matrix, are extensively used in deriving estimations and inferences. Random sample (independence) is assumed in most of the topics. Dependent sample will be covered in Advanced Econometrics II.
The students are expected to have a much deeper understanding of modern econometrics. The topics covered in this course are essential for rigorous economic research either empirically or theoretically.
Conditional Expectation and Projection (Hansen Chapter 2 & 3; Hong Chapter 2; Wooldridge Chapter 2)
- Conditional Expectation
- Best predictor under MSE criteria
- Linear projection and Least Squares Estimator
- Correct model specification
Single-Equation Linear Model (Hansen Chapter 4–7, 9; Hong Chapter 3–4; Wooldridge Chapter 3–4)
- Classical linear regression models: OLS and MLE
- Large sample linear regression models: OLS and Method of Moment
- Hypothesis testing
Single-Equation Linear Model with Instrumental Variables Estimation (Hansen Chapter 12 & 13; Wooldridge Chapter 5)
- Generalized method of moment under i.i.d.
- Two-stage least squares estimation
Multiple-Equation Model (Hansen Chapter 11; Wooldridge Chapter 7)
- Seemingly Unrelated Regressions: OLS and GLS
Linear Unobserved Effects Panel Data Models (Hansen Section 17.1–17.27; Wooldridge Chapter 10)
- FD, FE, RE methods
Assignments (40%)
Mid-term exam (30%)
Final exam (30%)
Google Classroom: u7wsnio
Lecture slides will be distributed. No single textbook will be exactly followed. Selected chapters from different textbooks will be listed as reading materials.