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54857-01 - Kolloquium: Machine Learning for Economists 3 KP

Semester Frühjahrsemester 2020
Angebotsmuster unregelmässig
Dozierende Anthony Strittmatter (anthony.strittmatter@unibas.ch, BeurteilerIn)
Inhalt Machine learning estimation methods gain more and more popularity. Compared to conventional estimation methods, machine learning can solve statistical prediction tasks in a data adaptive way. Furthermore, machine learning can deal with high-dimensional variable spaces in a relatively flexible way. Prediction methods are used in many different business and economic domains. Examples of prediction tasks are: The prediction of sales for a grocery store, such that logisticians can decide which products are shipped before they are sold. The prediction of the risk to become drug addicted, such that drug prevention programs can be targeted at adolescent with high risk.

Besides predictions, economists and managers are often interested in causal questions. Examples of causal questions are: Do tweets by president Donald Trump influence the oil prices? What impact has lowering the central bank interest rate on GDP? Does participation in training programs reduce the unemployment duration? Machine learning cannot give us an automatic answer to causal questions without an identification strategy. However, we can use machine learning to estimate nuisance parameters of different identification strategies in a flexible and data adaptive way. Furthermore, we can estimate heterogeneous effects with machine learning.

The course covers different predictive and causal machine learning methods. A focus will be on the application of these methods in practical programming session in R.

Lernziele Predictive Machine Learning:
- Regularized Regression
- Trees and Forests

Causal Machine Learning
- Double Selection Procedure
- Debiased Machine Learning
- Causal Forests

Optimal Policy Learning
Literatur James, Witten, Hastie, and Tibshirani (2014) "An Introduction to Statistical Learning", Springer.
Bemerkungen The course is for business and economics students.
Weblink Weblink to ADAM

 

Teilnahmebedingungen Basic knowledge of statistics and econometrics.
Anmeldung zur Lehrveranstaltung As the course is restricted to 25-30 students, please enrol by email to Anthony Strittmatter (anthony.strittmatter@unibas.ch) until 5 March, 2020. Enrolment = Registration for the exam!
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz
HörerInnen willkommen

 

Intervall unregelmässig
Datum 09.03.2020 – 20.03.2020
Zeit Siehe Detailangaben
Datum Zeit Raum
Montag 09.03.2020 12.15-16.00 Uhr Juristische Fakultät, Seminarraum S2 HG.35
Dienstag 10.03.2020 12.15-16.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39
Mittwoch 11.03.2020 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39
Mittwoch 18.03.2020 14.15-18.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S14 HG.32
Donnerstag 19.03.2020 12.15-16.00 Uhr Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39
Freitag 20.03.2020 12.15-16.00 Uhr Juristische Fakultät, Seminarraum S2 HG.35
Module Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät)
Leistungsüberprüfung Semesterendprüfung
Hinweise zur Leistungsüberprüfung tba.
An-/Abmeldung zur Leistungsüberprüfung An- und Abmelden: Dozierende
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,1
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch
Anbietende Organisationseinheit Wirtschaftswissenschaftliche Fakultät / WWZ

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