M.184.5489 Microeconometrics | |
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(Microeconometrics) |
Koordinator (coordinator): | Prof. Dr. Hendrik Schmitz |
Ansprechpartner (contact): | Prof. Dr. Hendrik Schmitz (hendrik.schmitz[at]uni-paderborn.de) |
Credits: | 10 ECTS |
Workload: | 300 Std (h) |
Semesterturnus (semester cycle): | WS |
Studiensemester (study semester): | 1-4 |
Dauer in Semestern (duration in semesters): | 1 |
Lehrveranstaltungen (courses): | ||||||
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Nummer / Name (number / title) |
Art (type) |
Kontaktzeit (contact time) |
Selbststudium (self-study) |
Status (P/WP) (status) |
Gruppengröße (group size) | |
a) | K.184.54891 / Microeconometrics | Vorlesung / Übung | 60 Std (h) | 240 Std (h) | P | 100 TN (PART) |
Wahlmöglichkeiten innerhalb des Moduls (Options within the module): | ||||||
Keine |
Empfohlene Voraussetzungen (prerequisites): |
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W1472 Grundzüge der Statistik II A basic knowledge of econometrics (the linear regression model as e.g. covered in „Introduction to Econometrics“ (Bachelor) or „Econometrics“ (Master) is assumed. We will start with a repetition of the linear regression model, but this will be rather quick and incomplete. |
Inhalte (short description): |
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The course teaches students basic methods to empirically analyze questions like … and many more by using micro data (e.g., individuals, households), empirical methods and statistical software. This is called "Microeconometrics" as opposed to "Macroeconometrics" or "Time series analysis" which typically uses aggregated data instead of individual level data. The questions above are "causal" questions and a major focus of the course is on methods to identify these causal effects. Students learn how to apply these methods using microdata and the software package Stata. |
Lernergebnisse (learning outcomes): |
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Fachkompetenz Wissen (professional expertise): |
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Fachkompetenz Fertigkeit (practical professional and academic skills): |
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Personale Kompetenz / Sozial (individual competences / social skills): |
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Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously): |
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Prüfungsleistungen (examinations) | |||
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Art der Modulprüfung (type of modul examination): Modulabschlussprüfung | |||
Art der Prüfung (type of examination) |
Umfang (extent) |
Gewichtung (weighting) | |
a) | Klausur | 120 Min. | 100.00 % |
Studienleistung / qualifizierte Teilnahme (module participation requirements) |
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Nein |
Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations) |
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Keine |
Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points) |
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Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist |
Gewichtung für Gesamtnote (calculation of overall grade) |
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Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1) |
Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes) |
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M.Sc. IBS, M.Sc. BWL, M.Sc. International Economics and Management, M.Sc. Management, M.Sc. Management Information Systems, M.Sc. Taxation, Accountingand Finance, M.Sc. Winfo, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik |
Umfang QT (participation requirements): |
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Lernmaterialien, Literaturangaben (learning material, literature): |
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Links to the video lecture, lecture slides, assignments, and data sets will be uploaded in advance on PANDA. Main textbooks: Cameron/Trivedi: Microeconometrics: Methods and Applications, 2005. Angrist/Pischke: Mastering Metrics: The Path from Cause to Effect, 2014. |
Teilnehmerbegrenzung (participant limit): |
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Keine |
Sonstige Hinweise (additional information): |
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Module cannot be combined with M.184.4489 Microeconometrics. This course is taught by the "inverted classroom" approach. This means that there will be no traditional lecture in the class room. Students are asked to either watch a video-lecture or read up the material in the textbook before the class starts. In class, four hours per week are devoted to discuss questions that arise in the video lecture or textbook, solve exercises, discuss empirical applications, and – if group size allows - work with the computer program and real data. Teaching language: English |