Lehre – Aktuelle Kurse
Frühjahrs-/Sommersemester 2023
E5114 Machine Learning and Statistical Learning
Course Title E5114 Machine Learning and Statistical Learning Course Type
Elective Course for M. Sc. Economics Responsible teacher of the module
Prof. Dr. Markus Frölich
ECTS Credits 7 Teaching method (hours per week) Lecture (2 SWS) + exercise (1 SWS) Workload 210 working hours, containing 31,5 hours class time and 178,5 hours independent study time Course language English Prerequisites Master: E601 – 603 (or equivalent) Grading exam (90 min, 70 %) and assignments (30 %) Goals and contents of the module Important topics of statistical learning and machine learning and applications in R. Start, End/
Time & LocationPlease find the latest data under our course catalog Examination
www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ Expected compentences acquired after completion of the module
Application of statistical learning models in R for data analysis Further information Further information: - We will have computer sessions in the computer lab. You may bring your own laptop. R is open source software.
- Core textbook: Elements of Statistical Learning (Hastie, Tibshirani, Friedman) and examples from „An Introduction to Statistical Learning“ (Jamesn, Witten, Hastie, Tibshirani)
Expected number of students in class 20 Contact Information: Name: Anja Dostert; Email: dostert@uni-mannheim.de; Office: L7, 3–5, room 1.21/1.22 E821 Topics in Empirical Development Economics
Course Title E821 Topics in Empirical Development Economics Course Type
Research Seminar (3 and 4th year) Responsible teacher of the module
Prof. Dr. Markus Frölich
ECTS Credits 5 Prerequisites Successful completion of first two years of PhD programme Start, End/
Time & LocationPlease find the latest data under our course catalog Requirements for the assignment of ECTS Credits and Grades
A written seminar paper on a topic of own choice and a presentation in class Examination
www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ Course content
Research seminar where Ph.D. students, who have completed their course work, present their own research and receive feedback. (Topics in empirical development economics with microeconometric methods. Development economics can be subdivided into three branches: Macro, micro theory and empirical with micro data. We only cover the last area. Macro and micro theory have been the driving forces of development economics initially, but with the increasing availability of micro data for Africa, Asia and Latin America in the last two decades, the foundation of the J-PAL network and the Nobel Prize in 2019, empirical development economics has been gaining attention.)
Competences acquired:
Doctoral Students will know how to
- identify a research question,
- put a research question into context of the relevant literature,
- present their current stage of research to their peers in a seminar environment.
Entwicklungsökonomie
Lehrmethode Blockseminar
Art und Verwendbarkeit des Moduls Wahlveranstaltung im Bachelorstudiengang Volkswirtschaftslehre Modulverantwortliche/r Prof. Dr. Markus Frölich
Semester
every term
Start, End/
Time & LocationPlease find the latest data under our course catalog ECTS-Punkte 6 Lehrmethode (Umfang) Blockseminar (2 SWS) Arbeitsaufwand Präsenzzeit Seminar: 21 Stunden; Zeit für die Anfertigung der Seminararbeit, für die Vorbereitung der Referate sowie für das Selbststudium 147 Stunden. Unterrichtssprache Deutsch
Teilnahmevoraussetzungen Grundlagen der Ökonometrie Examination
TBA Benotung schriftliche Seminararbeit (50%), Vortrag (25%), Koreferat (25%) Erwartete Zahl der Teilnehmer/ innen max. 13 Course description
Ziele und Inhalte des Moduls:
Das Seminar umfasst aktuelle Themen bezogen auf Arbeitsmärkte in Entwicklungsländern mit einem empirischen mikroökonometrischen Fokus. Die Themen beinhalten unter anderem: Kinderarbeit, informelle Arbeitsmärkte, Unternehmertum, die Schaffung von Firmen, Arbeitsmarktregulierungen, Mikrokredite, Mikroversicherungen, etc. Die Seminartermine werden nach den Wünschen der Studierenden ausgewählt. Die Studierenden sollen aktuelle Probleme von Entwicklungsländern erörtern und erkennen sowie empirische Studien zu diesen Fragen bewerten und diskutieren. In diesem Sinne ist es eine Mischung zwischen einem reinen Seminar zu Entwicklungsländern und einem angewandten Ökonometrieseminar. Die Studierenden sollen also auch angewandte ökonometrische Papiere verstehen, diskutieren und vorstellen, um die konkrete empirische Forschungsweise zu erlernen. Das Seminar ist insbesondere auch als eine Vorbereitung auf eine mögliche Bachelorarbeit im Bereich der angewandten empirischen Forschung gedacht, welche dann üblicherweise eine eigenständige ökonometrische Analyse mit Sekundärdaten verlangt. Das Seminar stellt somit eine Brückenfunktion zwischen den Grundlagenvorlesungen zur Ökonometrie, welche eher das Methodenwissen vermitteln, und der eigenständigen empirischen Analyse in der wissenschaftlichen Forschung, dar.Erwartete Kompetenzen nach Abschluss des Moduls Die Studierenden haben gelernt, einen Aufsatz zu einem Thema aus der Entwicklungsökonomie zu schreiben und zu präsentieren, wobei sie den Bezug zu mikroökonomischen Modellen und insbesondere empirisch-ökonometrischer Analyse herausgearbeitet haben. Dies umfasst somit auch eine kritische Analyse und Begutachtung von empirischen Studien und deren Methodik, insbesondere der Ökonometrie, der Datengrundlage und der Umsetzung der empirischen Herangehensweise. Weitere Informationen Bitte beachten Sie den gemeinsamen Anmeldezeitraum für Seminare des Bachelorstudiengangs VWL. Kontakt Prof. Dr. Markus Frölich, Tel. 0621/ 181–1920 (Sekretariat: Anja Dostert), E-Mail: dostert@uni-mannheim.de, L7, 3 – 5, Raum 1.21/1.22 E5031 Applied Labour Economics
Form and usability of the module Elective course for M. Sc. Economics Responsible teacher of the module
Dr. Asmus Zoch-Gordon, Marc Gillaizeau
Cycle of offer
Each spring semester
ECTS Credits 9 Teaching method (hours per week) Lecture (2) + exercises (2) Start, End/
Time & LocationPlease find the latest data under our course catalog Workload 270 hours in total, containing 42 hours class time and 238 hours for independent studies, project and exam preparation Course language
English
Prerequisites
E601 – E603 (or equvalent) Examination
www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ Grading
Written exam (100 min, 50%), take-home assignments (50%) Expected number of students in class 25 Goals and contents of the module
This course will focus on different micro-econometric models using actual empirical studies from the field of labour economics. Starting from the standard theory of competitive labour markets, we introduce the concept of human capital, to explain wage differences between individuals, and explore the role of education. Exploring the Mincer earnings function, discrimination and unemployment, the students will learn how to analyse actual labour data sets using Stata. The first part of the course will deal with linear panel data models and instrumental regressions, the second part will focus on discrete choice models. This course will end with the introduction of non-parametric estimators. Expected competences acquired after completion of the module: Ability to use Stata to conduct independent micro-econometric analysis and apply advanced micro-economic models. Further information Further information: Introductory literature:
• Wooldridge, Jeffrey M. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, Mass.: MIT Press. Chapters 10–20.
• George J. Borjas, Labor EconomicsContact information Dr. Asmus Zoch-Gordon; Phone: (0621) 181–1842; Email: zoch@uni-mannheim.de; Office: Room 123 Impact Evaluation
Form and usability of the module Elective course for B. Sc. Economics Responsible teachers of the module
Dr. Viviana Urueña, Dr. Benjamin Chibuye
Cycle of offer
Every spring semester
Duration 1 semester ECTS Credits 7 Start, End/
Time & LocationPlease find the latest data under our course catalog Teaching method (hours per week) Lecture (2) + exercise (2) Workload/
time in class
lecture 21 hours and exercise 21 hours, independent study time and preparation for the exam 154 hours Course language
English
Prerequisites
Statistik I + II, Grundlage der Ökonometrie Examination
www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ Grading
exam (90 minutes) and presentation, 80% final exam (90 minutes), 20% presentation (30 minutes including 5 minutes paper critique and 5 minutes group discussion). Goals and contents of the module The course is designed for introducing students to the main empirical strategies that are typically used for impact evaluation: Randomized Control Trials, Identification on Observables, Instrumental Variables, Difference-in-Difference, Regression Discontinuity Design. Students will be both exposed to fundamental concepts behind the estimation of causal effects and related applied applications. Students will be asked to actively participate and prepare a presentation once during the tutorial session. The lecture and the tutorial will take place every week. Lecture contents will be practiced during Stata exercise sessions in the tutorial or deepened with discussions of the current literature presented by students. Every participating student will have to present one research article once. The 30-minutes presentations (+/-10%) will contain a 20-minute summary of the paper and a 5-minute discussion of positive and negative paper aspects, potentially including secondary literature. Additionally, the presenting student will have to prepare 2–3 questions suitable to motivate a 5-minute group discussion with all course participants. In order to participate in the group discussions, all students are required to read the suggested literature before the tutorial sessions. Expected competences acquired after completion of the course • Understand what impact evaluation is and the different techniques used
• Understand the identifying assumptions underlying each impact evaluation technique
• Review the “parameters of interest”
• Make judgements about what specific impact evaluation technique is appropriate to use according to the context and type of interventionFurther information Main reading: Frölich, M.& Sperlich, S. (2019): Impact Evaluation – Treatment effects and causal analysis, Cambridge University Press.
Other useful material:
• Khandker S. et al. (2010): Handbook on Impact Evaluation: Quantitative Methods and Practices
• Caliendo M. and Kopeinig S. (2005): Some Practical Guidance for the Implementation of Propensity Score Matching
• Angrist, J., Imbens, G., and Rubin, D. (1996): Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455.
• Lee, D., Lemieux, T., Regression discontinuity designs in economics (2010). Journal of economic literature, 48 (2), 281–355.Maximum number of students in class 41 Contact Information:
Office: L7,3–5, room 131; Dr. Viviana Urueñat, E-mail: v.uruenauni-mannheim.de
Office L7,3–5, room 102; Dr. Benjamin Chibuye, E-mail: bchibuyemail.uni-mannheim.de
Statistics and Stata
Form and usability of the module elective course for B. Sc. Economics Responsible teachers of the module Dr. Nick Barton, Dr. Ingo Steinke Cycle of offer every spring term ECTS credits 7 Teaching method (hours per week) lecture (2) + exercise (2) Start, End/
Time & LocationPlease find the latest data under our course catalog Workload:
time in class:
lecture 21 hours and exercise 21 hours; independent study time and preparation for the exam 154 hours.
Course language
English
Prerequisites
Statistik I + II, Grundlagen der Ökonometrie Examination
www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ Grading programming exam (90 min.) Expected number of students in class depends on students´choice (max. 41) Goals and contents of the module: The course gives an introduction into the data management in Stata. That includes how to set up do-files, the preparation of data for analysis, the generation of variables, the use of macros in Stata, and the merging of data sets. Basic and advanced statistical procedures will be discussed in the course. For each model, there will be an introduction to the statistical model and it will be shown how to analyze the corresponding data with Stata and how to interpret the output of Stata. The models considered are some elementary statistical models, the linear regression model with homoscedastic and heteroscedastic error terms, analysis of variance models, linear panel data models, nonlinear regression models and binary and multinomial models. Expected competences acquired after completion of the module: The students know basic probabilistic and statistical concepts, e.g. the concept of a statistical test and how to compute and use p-values. The students can analyze data with Stata: The students are able to review a data set, generate summary statistics, and merge data sets. They know how to work with variables, matrices, and macros. They know how to perform elementary tests. The students can generate advanced plots. They are able to set up a linear model with homoscedastic or heteroscedastic error terms and understand the results provided by Stata. They can do an analysis of variance and test for heteroscedasticity in a linear regression model. They understand the ideas of linear panel data regression and can analyze corresponding data. The students are able to estimate the parameters, perform tests for the parameters, and analyze the results in nonlinear regression models and binary choice models. Further information: Literature: Cameron/ Trivedi (2009). Microeconometrics using Stata. Stata Press. Contact Information: Dr. Ingo Steinke; Phone: (0621) 181 1940; Email: isteinkerumms.uni-mannheim.de
Dr. Nicholas Barton; Email: bartonuni-mannheim.de