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Lehre – Aktuelle Kurse

Frühjahrs-/Sommersemester 2022

  • Statistics and Stata

    Form and usability of the moduleelective course for B. Sc. Economics
    Responsible teachers of the moduleDr. Atika Pasha, Dr. Ingo Steinke
    Cycle of offerevery spring term
    ECTS credits7
    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 classdepends 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: isteinke rumms.uni-mannheim.de

    Dr. Atika Pasha; Email: pasha uni-mannheim.de

  • Impact Evaluation

    Form and usability of the moduleElective course for B. Sc. Economics

    Responsible teachers of the module

    Dr. Katharina Richert, Dr. Benjamin Chibuye

    Cycle of offer

    Every spring semester

    Duration1 semester
    ECTS Credits7
    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 moduleThe 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 informationMain 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 class41

     

    Contact Information:

    Office: L7,3-5, room 131; Dr. Katharina Richert, E-mail: richert uni-mannheim.de

    Office L7,3-5, room 102; Dr. Benjamin Chibuye, E-mail: bchibuye mail.uni-mannheim.de

  • E5031 Applied Labour Economics

    Form and usability of the moduleElective course for M. Sc. Economics

    Responsible teacher of the module

    Dr. Asmus Zoch-Gordon, Marc Gillaizeau

    Cycle of offer

    Each spring semester

    ECTS Credits9
    Teaching method (hours per week)Lecture (2) + exercises (2)
    Start, End/
    Time & Location
    Please find the latest data under our course catalog
    Workload270 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 class25

    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 informationFurther 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 informationDr. Asmus Zoch-Gordon; Phone: (0621) 181-1842; Email: zoch uni-mannheim.de; Office: Room 123