Détails

Faculté	Faculté des sciences économiques et sociales et du management
Domaine	Economie politique
Code	UE-EEP.00142
Langues	Anglais
Type d'enseignement	Cours
Cursus	Master
Semestre(s)	SA-2019

Horaires et salles

Horaire résumé	Lundi , Cours bloc (Semestre d'automne)
Heures par semaine	3

Enseignement

Enseignants	Huber Martin
Assistants	Gangl Selina
Description	Firstly, this course discusses flexible or "nonparametric" econometric methods for data with many observations, where "flexible" implies that the researcher aims at imposing as few behavioral assumptions as possible. These methods are often more accurate than standard approaches such as OLS, which assumes a linear relation between the explanatory and dependent variables that might not hold in reality. Secondly, this course discusses so-called "machine learning" approaches to deal with data that include many variables, in order to optimally exploit the vast information provided in variables. Separating relevant from irrelevant information is key in a world with ever increasing data availability. The following topics will be covered in the course: * Flexible (non/semiparametric) vs. parametric models * Estimation methods: Kernel regression, series approximation, smoothing splines * Methods for choosing smoothing and bandwidth parameters * Testing: nonparametric specification and distribution tests * Shrinkage and variable selection: Lasso and ridge regression * Machine Learning: regression trees and random forests The lecture is accompanied by 4 PC sessions based on the software package "R", in which the methods are applied to empirical data.
Objectifs de formation	This course provides students with statistical methods for "big data", i.e. data sets that either have many observations or many variables or both. These methods are likely more accurate than "standard" econometric tools (such as OLS) which hinge on rather restrictive behavioral assumptions. Students should understand the intuition of the various methods (but are not required to formally reproduce tedious proofs) and how to practically implement them in the statistical software package “R” in order to investigate real world (big) data.
Conditions d'accès	introductory econometrics
Softskills	Non
Places softskills	10
Hors domaine	Non
BeNeFri	Oui
Mobilité	Oui
UniPop	Non

Documents

Bibliographie

Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press, ISBN: 9780691121611, 768 Pages Hastie, Tibshirani, Friedman (2009): “The Elements of Statistical Learning - Data Mining,Inference, and Prediction”, Springer. Scripts are also provided on the website of the course

Examen écrit - SA-2019, Session d'hiver 2020

Date	14.01.2020 11:00 - 12:30
Mode d'évaluation	Par note
Description	Examination time: 90 Min. and participation in PC labs

Examen écrit - SP-2020, Session de rattrapage 2020

Date	18.08.2020 11:00 - 12:30
Mode d'évaluation	Par note
Description	Examination time: 90 Min. and participation in PC labs