Advanced Econometrics
Course description and objectives
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
Contents
Resampling Methods (slides1)
Resampling Methods (slides1)
Pseudo-random generator
Monte Carlo experiments
Bootstrap method in regression
Permutation tests
Nonparametric Econometrics (slides2)
Nonparametric Econometrics (slides2)
Kernel density estimation
Kernel method for regression
Smoothing splines
Generalized Additive Models (GAM)
Mixture models
Econometrics & Machine Learning (slides3)
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