ECON450: Advanced Econometrics Winter, 2021
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ECON450: Advanced Econometrics
Winter, 2021
Course Description
This course focuses on machine learning. Topics studied include k-nearest neighbours, ridge regres- sion, LASSO, elastic net, random forests and neural networks.
Recommended Books
● Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009.
● Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning, Springer, 2014.
● Trevor Hastie, Robert Tibshirani, and Martin Wainwright, Statistical Learning with Sparsity, CRC Press, 2015.
● Bradley Efron and Trevor Hastie, Computer Age Statistical Inference, Cambridge University Press, 2016.
● Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
Prerequisites/Corequisites
Prerequisites: ECON351
Outline
1. Introduction
2. k-nearest neighbours
3. Ridge regression, LASSO and elastic net
4. Logistic regression
5. Neural networks
Other Topics
1. Classification trees and random forests
2. Kernel density estimation and kernel regression
3. Support vector machines
Evaluation
The final grade is a weighted average of the grade earned on the presentation, midterm project and term paper:
Tentative Evaluation Schedule
2022-01-28