Basic Requirements

Intermediate knowledge of econometrics and basic knowledge of programming with R.

Course description

These lectures have been conceived by econometricians for econometricians. The sessions proceed step by step, recalling the fundamental statistical concepts at the heart of the modern learning techniques. Their relative merits are illustrated by means of practical exercises and applications in R.

The course will present supervised Machine Learning techniques to econometricians. In particular, the lecturers will:

  1. present various concepts intensively used in the Machine Learning literature such as cross-validation, resampling, optimization routines
  2. describe and explain popular machine learning techniques such as bagging, random forests, boosting, SVM, neural nets and deep learning
  3. highlight their usefulness in econometrics for misspecification detection and causal inference.


Course (Emmanuel Flachaire)

Session Subject Schedule Supplementary Materials
Session 1 Introduction, Optimization, Overfitting, Cross-Validation 29 November 9.30-11.30
Session 2 Ridge and Lasso Regression 30 November 9.30-11.30 R: Ridge/Lasso
Session 3 CART, Bagging, Random Forests 6 December 9.30-11.30 R: Trees
Session 4 Boosting, Support Vector Machine 13 December 9.30-11.30 R: SVM
Session 5 Neural Networks, Deep Learning 20 December 9.30-11.30 R: Deep Learning
Session 6 Misspecification detection, Causal Inference 21 December 9.30-11.30 R: Misspecif. R: Causal Inf.

Hands-on (Ewen Gallic)

The hands-on session will use the programming language R. To run the codes you will need to download and install R and RStudio. More details on how to install R are available here.

Session Subject Schedule
Session 1 Gradient descent and overfitting 1 December 9.30-11.30
Session 2 Random forest and SVM 17 December 15.30-17.30
Session 3 Deep learning 21 December 14.30-16.30

The materials for the hands-on sessions are provided through an html book. A PDF version is also available. The R codes of each chapter can be downloaded (see below in the detailed programme).