Quantitative Analysis

This is the ?rst 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/?nancial data.

In addition to the conventional linear regression and maximum likelihood methods, various topics in statistical learning, such as LASSO, classi?cation 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 ?exible 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.

Reading
[S1] Kuan, C.-M., Lecture Slides, https://cool.ntu.edu.tw/login/; some videos are also available there.
[S2] Wooldridge, J. M., Introductory Econometrics, A Modern Approach, 6th Edition, Cegage Learning, 2016.
[S3] James, G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical
Learning with Applications in R, Springer, 2015.

R Programs
R program: https://cran.r-project.org/bin/windows/base/
R studio: https://www.rstudio.com/products/rstudio/download/
R tutorial: https://www.econometrics-with-r.org/index.html

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