課程資訊
 課程名稱 計量經濟學一Econometrics (Ⅰ) 開課學期 100-1 授課對象 管理學院  財務金融學研究所 授課教師 管中閔 課號 Fin8036 課程識別碼 723ED6000 班次 學分 3 全/半年 半年 必/選修 必修 上課時間 星期一6,7,8(13:20~16:20) 上課地點 管一203 備註 本課程以英語授課。總人數上限：25人 Ceiba 課程網頁 http://ceiba.ntu.edu.tw/1001econometrics 課程簡介影片 核心能力關聯 核心能力與課程規劃關聯圖 課程大綱 為確保您我的權利,請尊重智慧財產權及不得非法影印 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). 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 課程要求 預期每週課後學習時數 Office Hours 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. 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. 評量方式(僅供參考)
 課程進度
 週次 日期 單元主題 第2週 9/19 Syllabus; 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) 第3週 9/26 Slides: Linear Algebra (2011/09/26 update); Exercises for Practice in Linear Algebra 第4週 10/03 Classical Least Squares Theory (2011/10/02 update) 第6週 10/17 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) 第7週 10/24 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 第8週 10/31 Homework 2 of R 第9週 11/07 1. Lecture & Slide: CH.5: Elements of Probability Theory 第10週 11/14 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) 第11週 11/21 Lecture & Slide: Nonlinear Least Squares Theory (2011/11/28) 第12週 11/28 Slide: Ch.6: Asymptotic Least Squares Theory: Part I (2011/12/05 update) 第14週 12/12 Lecture & Slide Ch9: Quasi-Maximum Likelihood Theory 第16週 12/26 Ch10: Quasi-Maximum Likelihood: Applications 第17週 1/02 Introduction to Time Series Analysis (2012.01.02 update);previous exams