Teaching method (hours per week) | Lecture (2) + exercise (1) |
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Lecturer | Dr. Marc Gillaizeau, Dr. Viviana Urueña |
Semester | fall term term 2022 |
Target Audience | Elective course for M. Sc. Economics |
Start, End/ Time & Location | Please find the latest data under our course catalog |
Course language | English |
Prerequisites | E601- 603 (or equivalent). A background in development economics and Stata is helpful. |
Examination | www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ |
ECTS | 7 |
Grading and ECTS Credits | Presentation (20 min during tutorial, 20%) + Stata Assignments (home assignment in week 3 and 7, 25% each) + final essay exam (45 min, 30%) |
Course description | Goals and contents of the module: The course will introduce students to the main concepts of poverty and inequality measurements and the critical linksbetween poverty and inequality and economic growth. Students will get an overview on theories of justice, methodological aspects of poverty & inequality measurement, gender inequalities, economic mobility, inequality and poverty in rich countries as well as development policy targeting poverty. The course will focus on low- and middle-income countries. It is structured as follows: 1. Introduction 2. Long Run Determinants of Growth 3. Concepts and Measurements of Poverty I 4. Concepts and Measurements of Poverty II 5. Poverty Alleviation I: (Micro-)finance 6. Poverty Alleviation II: Cash transfers 7. Concepts and Measurements of Inequality 8. Does Inequality Cause Growth? 9. Pro-Poor Growth 10. Inequality and Gender 11. Poverty and Inequality in High-Income Countries 12. Economic Mobility 13. The Behavioral Economics of Poverty 14. Recap |
Expected competences acquired after completion of the module | The students become acquainted with the topics in poverty and inequality and learn to critically review and discuss empirical studies in the field. |
Expected number of students in class | 20 |
Contact information | Name: Dr. Marc Gillaizeau; Email: gillaizeau(at)uni-mannheim.de; Office: L7, 3–5, room 1.19; Office hours: on request via email |
Teaching method (hours per week) | Lecture (2) + Exercise (1) |
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Responsible teacher of the module | Prof. Dr. Markus Frölich |
Cycle of offer | Each fall semester |
ECTS Credits | 7 |
Form and usability of the module | Elective course for M.Sc. in Economics |
Start, End/ Time & Location | Please find the latest data under our course catalog |
Course language | English |
Prerequisites | E601 – 603 (or equivalent) |
Examination | www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ |
Grading | Written exam (120 min) |
Goals and contents of the module:
| This course will introduce students to theory and methods of modern impact evaluation. Topics will include counterfactual outcomes, heterogeneous treatment effects, (propensity) score matching, differences in differences, instrumental variables designs, randomized control trials, and regression discontinuity design. |
Expected competences acquired after completion of the module | The students are able to apply the main econometric models and estimators for impact evaluation and causal infererence and are able to analyze and judge causal inference identification strategies. |
Further information | Recommended literature: Impact Evaluation (Frölich, Sperlich, 2019, Cambridge University Press) |
Expected number of students in class | 20 |
Contact Information | Name: Anja Dostert; Email: dostert(at)uni-mannheim.de; Office: L7, 3–5, room 1.21/1.22 |
Teaching method (hours per week) | Lecture (3) and exercise (1) |
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Lecturer | Dr. Ingo Steinke; Dr. Nicholas Barton, PhD |
Semester | fall term 2022 |
Target Audience | Elective course for M. Sc. Economics |
Start, End/ Time & Location | Please find the latest data under our course catalog |
Course language | English |
Prerequisites | E601 – 603 (or equivalent) |
Examination | www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ |
ECTS | 9.5 |
Grading | Final exam (90 min) |
Course description | Goals and contents of the module: Although Stata already offers a large number of econometric tools, novel approaches are often not available and have to be implemented by users. This course offers an introduction to advanced programming in Stata. Since comparatively few people know how to do so, Stata programming skills can be a competitive advantage.The lecture will start with an introduction to efficiently written do-files (including data processing). We will look at and discuss different data types. In hands-on sessions students will be taught how to prepare the data for analysis. Variables will be generated and their distributions explored; data will be merged; and regression results will be critically discussed. Moreover, in this course students will learn how to implement new commands for Stata and to conduct Monte Carlo simulations. These are important for verification of implementations and are used as a very important tool to analyse the small sample properties of estimators and to complement the theoretical properties of estimators making them an integral part of econometric analyses. We will also touch upon Stata's matrix programming language Mata, non-linear optimization, e.g. ML estimation and bootstrap methods. |
Expected competences acquired after completion of the module | Students will be able to program quantitative methods using Stata independently. They are able to use Stata and Mata as programming languages and understand the standard syntax and the grammar of the languages. They will also be able to understand commands in Stata and edit these accordingly. Knowledge won from this module can be applied to various records. Students are capable of automatizing analysis and working efficiently. In addition to that, they will be able to conduct Monte Carlo simulations and interpret and use the results to estimate the quality of the estimation procedure. They can generate samples from a variety of distributions. Through Monte Carlo simulations, students will have a better comprehension of the uncertainty and quality of the estimation and test procedures. |
Further information | Cameron/ |
Expected number of students in class | 21 |
Contact information | Name: Dr. Nicholas Barton; Email: nibarton Name: Dr. Ingo Steinke; Email: isteinke rumms.uni-mannheim.de | mail.uni-mannheim.de
Teaching method (hours per week) | Lecture (4) + exercise (2) |
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Lecturer | Prof. Dr. Markus Frölich |
Semester | Each fall semester |
Form and usability of the module | Core course for M.Sc. Economics |
Start, End/ Time & Location | Please find the latest data under our course catalog |
Course language | English |
Prerequisites | Undergraduate level of econometrics |
Examination | www2.uni-mannheim.de/studienbueros/pruefungen/pruefungstermine/ |
ECTS | 10 |
Grading | Final exam (120 min, 100%) |
Course description | Goals and contents of the module: The goal of the module is to offer advanced treatment of econometric theory and to serve as the gate way to further advanced theoretical and applied econometric modules offered in the economics graduate program at the Department of Economics in Mannheim.The module offers a revision of undergraduate level econometrics before moving on to extensive coverage of large-sample theory and some organizing estimation principles such as GMM estimators. Asymptotic properties of these estimators are also the focus of the module as well as non-linear models and the treatment of serial correlation. |
Expected competences acquired after completion of the module | On successful completion of the module, students are expected to attain the following competences: • Attain advanced theoretical knowledge in econometrics in the specific topics the module covers at a high technical and mathematical level. • Be familiar with current theories and recent developments in the specific topics of focus for the module. • Attain a higher/ • Be in a position to take on follow-up advanced theoretical and applied econometrics modules. • Attain the level of competence that permits independent undertakings in search of new knowledge in the specialist areas the module covers. • Attain the level of competence required to carry out (theoretical) research-oriented projects independently. • To be in a position to exchange information, ideas, and solutions with experts of the field on a scientific level as well as with laymen. • To be able to communicate and to work effectively and efficiently with people and in groups. • Graduates are able to communicate precisely in the English specialist language. |
Further information | Recommended textbooks: •Econometrics; Bruce E. Hansen; University of Wisconsin; www.ssc.wisc.edu/~bhansen/econometrics/ |
Expected number of students in class | 65 |
Contact information | Name: Prof. Dr. Markus Frölich; Email: froelich(at)uni-mannheim.de; Office: L7, 3–5, room 114; Office hours: upon appointment |
Course Type | Research Seminar (3 and 4th year) |
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Lecturer | Prof. Dr. Markus Frölich |
Semester | fall term 2022 |
Prerequisites | Successful completion of first two years of PhD programme |
Start, End/ Time & Location | Please find the latest data under our course catalog |
Course language | English |
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/ |
ECTS | 5 |
Course description | Course content: Research seminar where Ph.D. students, who have completed their course work, present their own research and receive feedback. This seminar is intended to discuss topics around theoretical as well as applied research in the area of causal inference as well as randomized experiments and experimental design. Students are encouraged to review literature on a topic within this field, and explore if such research field may reflect or support their development of their own PhD project. Seminar topics normally refer to either Econometric Theory, i.e. identification or design development as well as estimators and their properties, or the applicability of methods that are linked to causal identification. Competences acquired: |
Lehrmethode | Blockseminar |
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Art und Verwendbarkeit des Moduls | Wahlveranstaltung im Bachelorstudiengang Volkswirtschaftslehre |
Modulverantwortliche/ | Prof. Dr. Markus Frölich |
Semester | every term |
Start, End/ Time & Location | Please 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/ | 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/ |
Form and usability of the module | elective course for B. Sc. Economics |
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Responsible teachers of the module | Dr. Atika Pasha, Dr. Ingo Steinke |
Cycle of offer | every spring term |
ECTS credits | 7 |
Teaching method (hours per week) | lecture (2) + exercise (2) |
Start, End/ Time & Location | Please 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/ |
Contact Information: | Dr. Ingo Steinke; Phone: (0621) 181 1940; Email: isteinke rumms.uni-mannheim.deDr. Atika Pasha; Email: pasha uni-mannheim.de |
Form and usability of the module | Elective course for B. Sc. Economics |
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Responsible teachers of the module | Dr. Katharina Richert, Dr. Benjamin Chibuye |
Cycle of offer | Every spring semester |
Duration | 1 semester |
ECTS Credits | 7 |
Start, End/ Time & Location | Please 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 intervention |
Further 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 • Angrist J. and Pischke, J. (2009): Mostly Harmless Econometrics • Angrist J. and Pischke, J. (2015): Mastering Metrics • 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. Katharina Richert, E-mail: richert uni-mannheim.deOffice L7,3–5, room 102; Dr. Benjamin Chibuye, E-mail: bchibuye mail.uni-mannheim.de |
Form and usability of the module | Elective course for M. Sc. Economics |
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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 & Location | Please find the latest data under our course catalog |
Workload | 270 hours in total, containing 33 hours class time and 237 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 Economics |
Contact information | Dr. Asmus Zoch-Gordon; Phone: (0621) 181–1842; Email: zoch(at)uni-mannheim.de; Office: Room 123 |