CC 502: Applied Econometrics
Lerninhalte
In this module we first briefly review most essential statistical concepts from descriptive and inferential statistics for univariate and bivariate data. Upon this, some concepts are extended or generalized to higher-dimensional data settings. The second part will mainly provide a treatment of the principles and uses of (linear) regression analysis for various purposes, such as causality analysis, prediction and forecasting. We will learn how the results from such analyses are appropriately interpreted and will discuss the limitations and potential pitfalls of all these techniques as well.
Lern- und Qualifikationsziele
By the end of the module students will have
- a sound understanding of key statistical concepts and techniques,
- familiarity with the principles and core techniques of econometric analysis and how regression results are used and interpreted,
- skills in the practical application of these techniques.
Notwendige Voraussetzungen
503, knowledge of basic statistics (elementary probability theory and inferential statistics included) at bachelor level required
Inhaltliche Voraussetzungen
knowledge of elementary linear algebra (vectors and matrices) helpful, should also know the concept of random variables and expected values
| Lehre | Selbststudium | |
|---|---|---|
| Vorlesung | 2 SWS | 6 SWS |
| Übung | 2 SWS | 7 SWS |
| ECTS | 6 | |
| Sprache | Englisch |
| Prüfungsform und -umfang | Written exam (90 min.) |
| Zulassungsbeschränkt | Nein |
| Informationen zur Anmeldung | – |
Geprüft durch Durchführende Lehrkraft |  | Dr. Toni Stocker Dr. Toni Stocker |
| Angebotsturnus | Herbst-/ |
| Dauer des Moduls | 1 Semester |
| Verwendbarkeit | M.Sc. MMM, M.Sc. WiPäd |
| Vorleistungen | – |
| Programmspezifische Kompetenzziele | CG 1, CG 2 |
| Benotung | Ja |
| Literatur | Handed out at the end of each lecture. Mainly based on Angrist, D.J., Pischke, J.-S. (2015) Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press Further reading:
|
| Gliederung | Introduction 1 Brief Overview of Course 2 Review of probability and statistics 2 OLS: Principles 3 Hypothesis Testing/ 4 OLS: Multiple Regression, Specifi cation 5 The OVB Problem and First Steps towards Causality 6 Instruments solve OVB (and many other problems...) 7 Differences-in-Differences Estimation 8 How Instruments solve OVB 9 Good Controls, Bad Controls, IV & Measurement Error 10 Recap & Your Questions |