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 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 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 Efficiency: 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)