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課程名稱 |
計量經濟理論三
Econometric Theory (Ⅲ) |
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開課學期 |
100-1 |
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授課對象 |
管理學院 國際企業學研究所 |
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授課教師 |
管中閔 |
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課號 |
ECON8005 |
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課程識別碼 |
323 M0570 |
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班次 |
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學分 |
4 |
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全/半年 |
半年 |
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必/選修 |
必修 |
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上課時間 |
星期一6,7,8(13:20~16:20)星期二3,4(10:20~12:10) |
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上課地點 |
社科2 |
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備註 |
二34為實習課。上課教室在管院，以財金所「計量經濟學一」公告之教室為主。國企所博士班財務組必修。
限碩士班以上
總人數上限：20人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1001econometric3 |
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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 |
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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 rst 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; econometrics at master level is not a prerequisite, however. In this course, I will follow my own lecture notes and cover various least-squares theories 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. What I hope is that, by introducing econometric theory in this way, students will learn how an econometric method is derived and why it works.
The lectures will be in English; classroom discussion may be in Mandrin if so desired. Students are also required to be familiar with at least one programming language, such as R 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). |
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課程目標 |
Course Outline
Part I: Review of Classical and Generalized 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)
I.6 Heteroskedasticity and Serial Correlation
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
Part III: Nonlinear Least Squares (NLS) Theory (Chapter 8 of R1; S1)
III.1 Nonlinear Speci cations
III.2 NLS Estimator
III.3 Asymptotic Properties of the NLS Estimator
III.4 Large Sample Tests
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 |
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課程要求 |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
Required Reading
R1. Kuan, C.-M., Introduction to Econometric Theory, Slides and Notes, available at:
https://ceiba.ntu.edu.tw/1001econometrics (for nance students)
https://ceiba.ntu.edu.tw/1001econometric3 (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 Speci cation Analysis, Cambridge University Press, 1994. |
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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.
S4. Kuan, C.-M., Elements of Matrix Algebra, Lecture Notes. |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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第1週 |
9/13 |
Syllabus |
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第2週 |
9/19,9/20 |
Lectures:
1. Ch1: Linear and Matrix Algebra(2011/09/18);
2. Ch2: Statistical Concepts (2011/09/18);
3. Ch3: Classical Least Squares Theory (2011/09/18);
4. Ch4: Generalized Least Squares Theory (2011/09/18);
Slides: 1. Linear Algebra (2011/09/18); 2. Classical Least Squares Theory (2011/09/18 16:05 update) |
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第3週 |
9/26,9/27 |
Slides: Linear Algebra (2011/09/26 update); Exercises for Practice in Linear Algebra |
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第4週 |
10/03,10/04 |
Classical Least Squares Theory (2011/10/02 update) |
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第6週 |
10/17,10/18 |
1. Classical Least Squares Theory (2011/10/16 update), 2. 自殺率與相關變數的資料 (Data of suicide rate & other variables ) (2011/10/16) |
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第7週 |
10/24,10/25 |
1. R Installation Instructions (2011/10/23); 2. Getting started with R (2011/10/24); 3. Classical Least Squares Theory (2011/10/24 update);4. Homework of R (2011/10/24); 5. R code |
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第8週 |
10/31,11/01 |
Homework 2 of R |
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第9週 |
11/07,11/08 |
1. Lecture & Slide: CH.5: Elements of Probability Theory |
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第10週 |
11/14,11/15 |
Lecture & Slide: Ch.6: Asymptotic Least Squares Theory: Part I; Correction of the simulation (p.17 in the lecture note:Getting Started With R); R Note & Homework (LLN& CLT) |
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第11週 |
11/21,11/22 |
Lecture & Slide: Nonlinear Least Squares Theory (2011/11/28) |
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第12週 |
11/28,11/29 |
Slide: Ch.6: Asymptotic Least Squares Theory: Part I (2011/12/5 update) |
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第14週 |
12/12,12/13 |
Lecture & Slide Ch9: Quasi-Maximum Likelihood Theory |
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第16週 |
12/26,12/27 |
Ch10: Quasi-Maximum Likelihood: Applications |
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第17週 |
1/02,1/03 |
Introduction to Time Series Analysis (2012.01.02 update);previous exams |
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