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54857-01 - Kolloquium: Machine Learning for Economists and Business Analysts (3 KP)
| Semester | Frühjahrsemester 2022 |
| 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 ship products before they are sold. The prediction of the probability to become drug addicted later in life, such that drug prevention programs can be targeted at adolescents with high risk. Besides predictions, economists and managers are often interested in causal questions. Examples of causal questions are: What are the effects of tweets by Elon Musk on Bitcoins? 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 using an empirical design. However, machine learning estimates can serve as input factors for these empirical designs. 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 R programming session. Predictive Machine Learning: - Regularized Regression - Trees and Forests - Unsupervised Machine Learning Causal Machine Learning - Double Selection Procedure - Debiased Machine Learning - Causal Forests - Optimal Policy Learning - Reinforcement Learning |
| Lernziele | 1) Students can distinguish between questions that can be answered with predictive and causal methods. 2) Students can implement predictive machine learning estimators in R. 3) Students can deploy machine learning methods to account for control variables. 4) Students can estimate heterogeneous effects with causal forests. 5) Students know different machine learning approaches that can be used to estimate decision rules and can apply these approaches to economic and business problems. |
| Literatur | James, Witten, Hastie, and Tibshirani (2014) "An Introduction to Statistical Learning", Springer. Free download: http://www-bcf.usc.edu/~gareth/ISL/ Sendhil Mullainathan and Jann Spiess, 2017, Machine Learning: An Applied Econometric Approach, Journal of Economic Perspectives, 31(2), 67-106. Susan Athey, 2017, Beyond Prediction: Using Big Data for Policy Problems, Science, 335 (6324), 483-485. Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, 2017, Double/Debiased/Neyman Machine Learning of Treatment Effects, American Economic Review, 107(5), 261-265. More literature references will be provided during the lecture |
| Bemerkungen | The course is for business and economics students. |
| Weblink | Weblink to ADAM |
| Teilnahmevoraussetzungen | Basic knowledge of statistics and econometrics. |
| Anmeldung zur Lehrveranstaltung | Registration: Please enrol in MOnA. EUCOR-Students and students of other Swiss Universities have to enrol at the students administration office (studseksupport1@unibas.ch) within the official enrolment period. Enrolment = Registration for the exam! The course is limited to 24 participants. If an admission should not be possible for this reason, you will be notified after the deadline for enrollment, i.e. on March 22, 2022. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| unregelmässig | Siehe Einzeltermine | ||
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Mittwoch 30.03.2022 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Donnerstag 31.03.2022 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Freitag 01.04.2022 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Montag 11.04.2022 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S14 HG.32 |
| Dienstag 12.04.2022 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Mittwoch 13.04.2022 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Module |
Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät) |
| Prüfung | Leistungsnachweis |
| Hinweise zur Prüfung | Individual Home Assignment |
| An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |