The aim of this course is to provide the participants with a basic understanding of empirical economics and to give them an introduction to econometrics. Building on the lecture "BA: Statistics" the participants shall be enabled to conduct empirical analysis on their own.
Analysis of economic relationships
Introduction to econometrics
Introduction to STATA
Estimating, testing and predicting in the simple and multiple regression model framework
Problems and extensions of the multiple regression model
Wooldridge, J. (2013): Introductory Econometrics. A Modern Approach. South-Western Cengage Learning.
Schira, J. (2012): Statistische Methoden der VWL und BWL. Pearson Studium.
Kohler, U., Kreuter, F. (2008): Datenanalyse mit Stata. Oldenburg Verlag.
Active participation and presentation in practical sessions
Foundations in Mathematics and Statistics are essential. For the course in multivariate time series econometrics, taking univariate time series econometrics is highly recommended.
This course deals with time series econometric methods that are mainly applied in the fields of Macroeconomics and Finance. The lecture and the tutorials will be held in English. Formally, this course consists of two separate courses (univariate and multivariate time series econo- metrics) which will have separate exams at the end of the semester. Models of univariate and multivariate time series with stationary and non-stationary processes will be presented. Students learn methods and tools for analyzing univariate and multivariate time series and apply them in the computer tutorials to recent, real world data.
Univariate time series models:
Augmented distributed lag models
Integrated and cointegrated processes.
Multivariate time series models:
VAR und VECM
Identification and analysis of structural shocks: SVAR
Enders, W. (2004): Applied Econometric Time Series Analysis. Wiley.
Hamilton, J.D. (1994): Time Series Analysis. Princetion University Press.
Kirchgässner, G., Wolters, J., and Hassler, U. (2013): Introduction to Modern Time Series Analysis. Springer.
Lütkepohl, H. (2007): New Introduction to Multiple Time Series Analysis. Springer.
31.01.20, 14:00-15:30: Wrap-up and final discussion (room: Haus 7, 2.10)
31.01.20: Empirical assignment sent out
7.2.20, midnight: Empirical assignment paper due
Economics: MA-FK-600, MA-W-210/220
MA: Public Policy Evaluation recommended
Actively participating in all sessions and complying with all deadlines listed in the schedule.
Complete the reading assignments for the “Introduction to Machine Learning” Sessions.
Present one empirical application.
Complete two empirical problemsets.
Complete the final empirical assignment.
Your grade will be determined by how well you do in your presentation, in participating in the discussion, in the problemsets, and in the final empirical assignment.
Title: "Topics in Machine Learning and Econometrics"
This seminar provides a broad overview of the main concepts of machine learning, especially supervised learning, and how they can enhance causal inference. We will not only discuss recent empirical economics papers applying machine learning methods, but also explore how to implement these methods in R. Students will have the chance to get to know R in a Workshop organized by PCQR and/ or through online courses provided by “Datacamp for the classroom”. During the semester students will present one empirical application and complete two problemsets. The final assignment will be in the spirit of a Machine Learning Challenge. Throughout the course, students have the chance to practice public speaking and presenting empirical results intuitively as well as getting hands-on experience in R. Furthermore, this course will enable students to follow-up on new developments in this quickly evolving field on their own.