Advanced Econometrics

Course description and objectives

The goal of this course is to present advanced methods in econometrics for distributional analysis, regression and classification models.

Syllabus : pdf

Contents

Resampling Methods (slides1)

• Pseudo-random generator

• Monte Carlo experiments

• Bootstrap method in regression

• Permutation tests

Nonparametric Econometrics (slides2)

• Kernel density estimation

• Kernel method for regression

• Smoothing splines

• Generalized Additive Models (GAM)

• Mixture models

Econometrics & Machine Learning (slides3)

• General principle, overfitting and cross-validation

• Ridge and Lasso regression

• Random forests, boosting, support vector machine and neural networks

• Model misspecification detection

• Causal Machine Learning