課程資訊
課程名稱
計量經濟學一
Econometrics (Ⅰ) 
開課學期
103-1 
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
全/半年
半年 
必/選修
必修 
上課時間
星期一A,B,C(18:25~21:05) 
上課地點
管一402 
備註
本課程以英語授課。
總人數上限:50人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1031econometrics02 
課程簡介影片
 
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課程概述

This is the first course in econometric theory for Ph.D. students; master students and undergraduates with proper mathematics and statistics background (e.g., probability theory, multivariate statistics, and matrix algebra) are welcome to take this course. Please check R1 (Chapters 1 and 2 of Lectures Notes) and S3 below for related background information. Some basic econometrics knowledge is a plus but not required.
In this course, I will follow my own lecture notes and cover the least-wquares 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 different models. More detailed treatment of these theories can be found in R2 and R3 below. By introducing econometrics in this way,I hope students can understand how an econometric method is derived and why it works.
The lectures will be in English; classroom discussion may be in Mandrin. Students are required to learn a 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
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;S1)
II.1 Elements of Probability Theory
II.2 Asymptotic Properties of the OLS Estimator
II.3 Consistent Estimation of Covariance Matrix
II.4 Large Sample Tests
II.5 Digression: Bootstrap

Part III: Nonlinear Least Squares (NLS) Theory (Chapter 8 of R1; S1)
III.1 Nonlinear specifications
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.6 Microeconometric models
IV.7 Applications: ARMA models
IV.8 Applications: Volatility models 
課程要求
待補 
預期每週課後學習時數
 
Office Hours
 
參考書目
R1 Kuan, C.-M., Introduction to Econometric Theory, Lecture Notes and Slides. https://ceiba.ntu.edu.tw/1031econometrics02 (for finance students) https://ceiba.ntu.edu.tw/1031econometrics01 (for economics and IB students)
R2 White, H., Asymptotic Theory for Econometricians, revised ed., Academic
Press, 1999.
R3 White, H., Estimation, Inference and Specification Analysis, Cambridge
University Press, 1994.
S1. Greene, W. H., Econometric Analysis, 6th ed., Pearson Prentice Hall, 2008.
S2. Hamilton, J., Time Series Analysis, Princeton University Press, 1994.
S3. Kuan, C.-M., Elements of Matrix Algebra, Lecture Notes. 
指定閱讀
待補 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
9/15  Syllabus 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; Ch8: Nonlinear Least Squares Theory; Ch9: Quasi-Maximum Likelihood Theory; Ch10: Quasi-Maximum Likelihood: Applications;