課程資訊
課程名稱
 計量經濟理論一
Econometric Theory (Ⅰ) 
開課學期
 103-1 
授課對象
 社會科學院  經濟學研究所  
授課教師
 李宗穎 
課號
 ECON7014 
課程識別碼
 323EM6140 
班次
  
學分
 2 
全/半年
 半年 
必/選修
 必修 
上課時間
 星期三3,4(10:20~12:10) 
上課地點
 社科406 
備註
 本課程以英語授課。
限碩士班以上
總人數上限：70人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/1031ECON7014 
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課程概述

This course focuses mainly on the identi cation, speci cation, estimation and inference of econometric models. Linear regression and quantile regression are introduced fi rst and discussed in detail. Advanced topics include generalized method of moments, instrumental variables, and econometric methods for panel data relevant for empirical research in economics and finance. 

課程目標
 1. Introduction (A1&2)
2. Least Squares and Quantile Regression (H1, A3)
3. Finite-sample Properties of Least Squares; Bootstrap and Subsampling
4. Asymptotic Theory for Least Squares and Regression Quantiles (H2.1-2.9)
5. Semiparametric Efficiency: Least Squares
6. Endogeneity and Instrumental Variables (A4, Angrist and Pischke 2010, Roberts and Whited 2011)
7. Building Blocks of Time Series Models
8. Single-Equation Linear GMM (H3)
9. GMM: Consumption-based Asset Pricing (H7, C5, Newey and McFadden 1994)
10. Discrete Response Models
11. Panel Data Econometrics (W10, C8, Petersen 2009) 
課程要求
 The course grade will be based on weekly problem sets, a midterm, and a nal. The assignments will consist of both theoretical and programming exercises which can be done in Matlab, Stata, R, or any other econometric software. Students should be prepared in matrix algebra and mathematical statistics at the level of Econ7009 or equivalent. 
預期每週課後學習時數
  
Office Hours
 每週二 09:30~11:00 
指定閱讀
  
參考書目
 See syllabus 
評量方式
(僅供參考)
    
課程進度
週次
日期
單元主題
Week 1
 9/17  Probability theory, conditional probability, Bayes' theorem, law of total probability, independence 
Week 2
 9/24  Random variables, distribution functions: joint, conditional, marginal 
Week 3
 10/01  Properties of random variables: expectation, variance, other moments, independence, correlation 
Week 4
 10/08  Some selected distributions 
Week 5
 10/15  Introduction to inference: finite sample inference and large sample inference 
Week 6
 10/22  Large sample inference 
Week 7
 10/29  Estimators: consistency, asymptotic normality, efficiency, and asymptotic efficiency 
Week 8
 11/05  Maximum likelihood estimator (MLE) 
Week 9
 11/12  Midterm exam (in class) 
Week 10
 11/19  Hypothesis testing: introduction 
Week 11
 11/26  Trinity in hypothesis testing: Lagrange multiplier, Wald, and likelihood ratio tests 
Week 12
 12/03  Classical linear regression theory 
Week 13
 12/10  Small sample results for the linear regression model 
Week 14
 12/17  Large sample results for the linear regression model 
Week 15
 12/24  Large sample results for the linear regression model 
Week 16
 12/31  Generalized method of moments (GMM): identification, consistency, asymptotic distribution 
Week 17
 1/07  Generalized method of moments (GMM): identification, consistency, asymptotic distribution 
Week 18
 01/14  Final exam (in class)