課程名稱 |
計量經濟理論三 Econometric Theory (Ⅲ) |
開課學期 |
101-1 |
授課對象 |
社會科學院 經濟學研究所 |
授課教師 |
管中閔 |
課號 |
ECON8005 |
課程識別碼 |
323EM0570 |
班次 |
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學分 |
4 |
全/半年 |
半年 |
必/選修 |
必修 |
上課時間 |
星期一6,7,8(13:20~16:20)星期三3,4(10:20~12:10) |
上課地點 |
社科6 |
備註 |
本課程以英語授課。三34為實習課。正課上課教室在管院,以財金所「計量經濟學一」公告之教室為主。 限碩士班以上 總人數上限:20人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1011metrics_ib |
課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
This is the first course in econometric theory for Ph.D. students; well prepared Master students are also welcome to take this course. This course requires knowledge of probability theory, multivariate statistics, and linear (matrix) algebra. Some econometrics knowledge at master level is a plus but not required. In this course, I will follow my own lecture notes and cover the least-squares theory and quasi-maximum likelihood theory. Unlike most econometrics textbooks that are organized according to models, my notes are arranged by theories (methods), with applications to various models. Some textbooks (R2 and R3 below) provide more thorough treatment of these topics. By introducing econometric theory in this way, I hope students will learn how an econometric method is derived and why it works. In addition, commonly used computational methods in econometrics, such as Monte Carlo simulation and bootstrap, will also be introduced.
The lectures will be in English; classroom discussion may be in Mandrin. To conduct simulations and bootstraps, students are required to learn at least one programming language, such as R (a free software) or Matlab. A senior student will introduce basic programming in R in the beginning lectures; some basic materials about R installation and introduction can be found in the class website (see below). |
課程目標 |
Course Outline
Part I: Classical Least Squares Theory (Chapters 3-4 of R1; S2; S4)
I.1 The Method of Ordinary Least Squares (OLS)
I.2 Properties of the OLS Estimator
I.3 Hypothesis Testing
I.4 Limitation of the Classical Conditions
I.5 The Method of Generalized Least Squares (GLS)
Part II: Asymptotic Least Squares Theory (Chapters 5-7 of R1; R2; R3)
II.1 Elements of Probability Theory
II.2 Asymptotic Properties of the OLS Estimator
II.3 Consistent Estimation of Asymptotic Covariance Matrix
II.4 Large Sample Tests
II.5 Digress: Bootstrap
Part III: Nonlinear Least Squares (NLS) Theory (Chapter 8 of R1; S1)
III.1 Nonlinear Specications
III.2 NLS Estimator
Part IV: Quasi-Maximum Likelihood (QML) Theory (Chapters 9-10 of R1; R3; S3)
IV.1 Kullback-Leibler Information Criterion
IV.2 Asymptotic Properties of the QML Estimator
IV.3 Information Matrix Equality
IV.4 Large Sample Tests - Nested Models
IV.5 Large Sample Tests - Non-Nested Models
IV.7 Applications: ARMA Models
IV.8 Applications: Volatility Models |
課程要求 |
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預期每週課後學習時數 |
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Office Hours |
另約時間 備註: Tuesday 4-6 or by appointment (3366.1072) |
指定閱讀 |
Required Reading
R1. Kuan, C.-M., Introduction to Econometric Theory, Slides and Notes, available at:
https://ceiba.ntu.edu.tw/1011metrics n (for nance students)
https://ceiba.ntu.edu.tw/1011metrics ib (for economics and IB students)
homepage.ntu.edu.tw/ckuan
R2. White, H., Asymptotic Theory for Econometricians, revised ed., Academic Press, 1999.
R3. White, H., Estimation, Inference and Specication Analysis, Cambridge University Press, 1994. |
參考書目 |
Supplemental Reading
S1. Davidson, R. and J. G. MacKinnon, Estimation and Inference in Econometrics, Oxford University Press, 1993.
S2. Greene, W. H., Econometric Analysis, 6th ed., Pearson Prentice Hall, 2008.
S3. Hamilton, J., Time Series Analysis, Princeton University Press, 1994.
S4. Kuan, C.-M., Elements of Matrix Algebra, Lecture Notes. |
評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
Midterm |
40% |
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2. |
Final |
45% |
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3. |
Homework |
15% |
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週次 |
日期 |
單元主題 |
第1週 |
9/10,9/12 |
1. Syllabus (2012.09.09 update);
2. R Installation Instructions (2012.09.09);
3. Getting started with R (2012.09.09) |
第2週 |
9/17,9/19 |
Slide: Classical Least Squares Theory; Elements of Probability Theory; Asymptotic Least Squares Theory. Lecture: Ch1: Linear and Matrix Algebra, Ch2: Statistical Concepts, Ch3: Classical Least Squares Theory, Ch4: Generalized Least Squares Theory; Ch5: Elements of Probability Theory; Ch6: Asymptotic Least Squares Theory: Part I. R: lecture, code and homework |
第3週 |
9/24,9/26 |
R: code and homework |
第5週 |
10/08,10/10 |
Lecture: Ch8: Nonlinear Least Squares Theory, Ch9: Quasi-Maximum Likelihood Theory, Ch10: Quasi-Maximum Likelihood: Applications; Slide: Nonlinear Least Squares Theory, Quasi Maximum Likelihood Theory |
第6週 |
10/15,10/17 |
Slide: Classical Least Squares Theory (2012.10.14 update); R: code & homework |
第13週 |
12/03,12/05 |
Introduction to Time Series Analysis (2012.12.03) |
第16週 |
12/24,12/26 |
Exam-ET_2010 (2012.12.24); Exam-ET_2011 (2012.12.24) |
第17週 |
12/31,1/02 |
Introduction to Time Series Analysis (2012.12.30 update) |
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