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課程名稱 |
計量分析
Quantitative Analysis |
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開課學期 |
108-2 |
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授課對象 |
管理學院 財務金融學研究所 |
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授課教師 |
管中閔 |
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課號 |
Fin7047 |
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課程識別碼 |
723EM9000 |
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班次 |
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學分 |
3.0 |
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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 |
星期一7,8,9(14:20~17:20) |
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上課地點 |
管一402 |
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備註 |
本課程以英語授課。與楊睿中合授
限碩士班以上
總人數上限：50人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1082Fin7047_QA |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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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 first course in econometrics for master students; undergraduate students with
proper statistics and mathematics background are welcome to take this course.
This course is designed to prepare students with basic knowledge of econometric and
statistical (machine) learning methods that are useful for analyzing economic/financial data.
In addition to the conventional linear regression and maximum likelihood methods, various
topics in statistical learning, such as LASSO, classification and regression trees, random
forests, and neural networks, will also be covered. These learning methods have been widely
applied to extract features from very large data sets (“big data”) and are now popular in
practice.
This course requires programming in R, which is a flexible tool for econometric analysis
and computational tasks. Our TA will introduce basic ideas in R coding every week and
give coding exercises for practice. Our exams will also include problems in R codes. |
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課程目標 |
Course Outline
Lecture 1 Economic Data and Simple Linear Regression (S2, Chap. 1, 2)
Lecture 2 Multiple Linear Regression: Estimation (S2, Chap. 3)
Lecture 3 Multiple Linear Regression: Inference (S2, Chap. 4, 7)
Lecture 4 Multiple Linear Regression: Asymptotics (S2, Chap. 5, 8)
Lecture 5 Maximum Likelihood Method and Discrete Choice Models (S2, Chap. 17)
Lecture 6 Resampling Methods (S3, Chap. 5)
Lecture 7 Linear Model Selection and Regularization (S3, Chap. 6)
Lecture 8 Moving Beyond Linearity (S3, Chap. 7)
Lecture 9 Tree-Based Models (S3, Chap. 8)
Lecture 10 Neural Networks (Notes by Prof. Yang)
Self Study Text Mining and Applications
Office Hours: TA (Ts-Mou Hwu) will hold office hours on Friday 2:00–5:00, Room
415, Management School Bldg. 2; you may also contact him at r07723044@ntu.edu.tw for
appointment. |
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課程要求 |
Grading: Homework assignments (20%) and two exams (40% each); for questions about
homework grading please contact An-Mei Tsai at r08723023@ntu.edu.tw. |
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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 |
Reading
[S1] Kuan, C.-M., Lecture Slides, https://cool.ntu.edu.tw/login/; some videos are also avail-
able there.
[S2] Wooldridge, J. M., Introductory Econometrics, A Modern Approach, 6th Edition, Ce-
gage Learning, 2016.
[S3] James, G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical
Learning with Applications in R, Springer, 2015. |
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參考書目 |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Homework assignments |
20% |
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2. |
Exam |
40% |
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3. |
Exam |
40% |
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