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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: ECON351



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



The final grade is a weighted average of the grade earned on the presentation, midterm project and term paper:

Tentative Evaluation Schedule