Details

Faculty	Faculty of Management, Economics and Social Sciences
Domain	Economics
Code	UE-EEP.00142
Languages	English
Type of lesson	Lecture
Level	Master
Semester	SA-2019

Schedules and rooms

Summary schedule	Monday , Cours bloc (Autumn semester)
Hours per week	3

Teaching

Teachers	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.
Training objectives	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.
Condition of access	introductory econometrics
Softskills	No
Softskills seats	10
Off field	No
BeNeFri	Yes
Mobility	Yes
UniPop	No

Documents

Bibliography

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

Written exam - SA-2019, Session d'hiver 2020

Date	14.01.2020 11:00 - 12:30
Assessments methods	By rating
Descriptions of Exams	Examination time: 90 Min. and participation in PC labs

Written exam - SP-2020, Session de rattrapage 2020

Date	18.08.2020 11:00 - 12:30
Assessments methods	By rating
Descriptions of Exams	Examination time: 90 Min. and participation in PC labs