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290047 VU Advanced Quantitative Data Analysis and Microeconometrics (2021S)

4.00 ECTS (2.00 SWS), SPL 29 - Geographie
Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The digital exam will take place during the final session

Thursday 25.03. 15:00 - 17:00 Digital
Tuesday 13.04. 09:30 - 11:30 Digital
Thursday 15.04. 15:00 - 17:00 Digital
Monday 19.04. 09:30 - 11:30 Digital
Wednesday 21.04. 09:30 - 11:30 Digital
Wednesday 28.04. 09:30 - 11:30 Digital
Monday 03.05. 09:30 - 11:30 Digital
Wednesday 05.05. 09:30 - 11:30 Digital
Monday 10.05. 09:30 - 11:30 Digital
Wednesday 12.05. 09:30 - 11:30 Digital
Wednesday 19.05. 09:30 - 11:30 Digital
Wednesday 26.05. 09:30 - 11:30 Digital

Information

Aims, contents and method of the course

Microeconometric methods are frequently used to answer important research questions in the social sciences, such as

• How does immigration affect domestic labour markets?
• Why are women paid less than men?
• What are the (causal) effects of educational attainment on income, health, and mortality?
• Does time spent in prison prevent future criminal behaviour?

This course is targeted at students with an interest in applied empirical research and will prepare them to answer their own research questions by carefully choosing their data, appropriate survey and microeconometric methods, and effective data visualisation.

The course will start by introducing students to different data types and data sources frequently used for microeconometric analyses in the social sciences. Next, we continue to discuss the most important quality criterions of these data and explore how potential flaws can be accounted for with survey methods. We then proceed to the largest part of the course, which will deepen students’ knowledge about quantitative data analysis, focusing on microeconometrics. More specifically, we will cover

• General linear models for different data types
• Decomposition methods
• Panel data analysis with fixed and random effects models
• Causal inference from instrumental variables, difference-in-difference analyses, and regression discontinuity designs
• Survival analysis (optional)

Once students know how to choose the appropriate data and methods for a specific research problem, we will discuss how related results can be presented effectively considering data visualisation basics.

METHODS: The lecturer will introduce students to different data sources, survey methods and estimation techniques, thereby mainly focusing on the intuition behind the respective methods. In addition, students will learn how to critically evaluate the implementation of these methods by reading, presenting, and discussing topical research articles. They will apply the newly learned methods by analysing data during a take-home assignment.

LEARNING OUTCOME: After this course, students will (i) know the most important data types and sources for microeconometric analyses in the social sciences, (ii) know how flawed data can be accounted for using survey methods, (iii) be able to identify appropriate estimation techniques to analyse these data, in particular microeconometric methods, (iv) know how to present their results considering best practices in data visualisation, and (v) be able to critically evaluate research designs considering data, methods, interpretation and visualisation of the results.

COURSE AND EXAMINATION LANGUAGE: English

PREREQUISITES: Students should have basic training in statistics including linear and non-linear regression. Basic knowledge of a statistical software (e.g., Stata or R) is necessary to complete parts of the take-home assignment (see performance components below). The software will not be covered in class. The lecturer will, however, provide introductory online materials if students wish to self-study the basics.

Assessment and permitted materials

The performance components consist of (i) an exam, (ii) one take-home assignment, (iii) one presentation of research articles as well as (iv) active class participation:

(i) Exam (40%): The exam will take place during the final session (digital or on-site, depending on the COVID-19 related regulations).
(ii) Take-home assignment (20%): Students will apply what they have learned to a dataset provided by the lecturer, using a statistical software of their choice (ideally Stata or R). They will submit their code and answer questions based on their analysis (potentially in groups). The assignment will be discussed in class after the submission deadline and before the final exam.
(iii) Presentation of research articles (20%): Students will present and critically review one or two research articles from the reading list in class (potentially in groups).
(iv) Active class participation (20%): Students are asked to actively participate in class. Moreover, they are expected to read weekly assigned research articles and submit one discussion question per research article for in-class discussions.

Minimum requirements and assessment criteria

For a successful completion of the course, all performance components must be delivered in time and passed individually (at least 50% per performance component). The final grade will be determined as follows

89%-100%: Excellent (1)
76%-88%: Good (2)
63%-75%: Satisfactory (3)
50%-62%: Sufficient (4)
<50%: Unsatisfactory (5)

Attendance is compulsory; up to two absences will be excused if the lecturer is informed beforehand.

Examination topics

• Content of the lectures and the take-home assignment
• Assigned research articles and presentations thereof
• Selected book chapters

Reading list

Weekly assigned research articles will be announced in class in due time. In addition, selected chapters from the following books will help students to prepare for class, the take-home assignment, and the exam. Access to these chapters will be provided in due time as well.

• Mostly harmless econometrics: An empiricist's companion (Angrist & Pischke, Princeton University Press)
• Mastering 'Metrics: The Path from Cause to Effect (Angrist & Pischke, Princeton University Press)
• A Guide to Econometrics (Kennedy, Wiley)
• Survey methodology (Groves et al., Wiley)

Association in the course directory

(MG-S3-PI.m) (MG-S6-PI.m) (MG-S4-PI.m) (MG-S5-PI.m) (MG-W3-PI) (MR1-a-PI)

Last modified: Th 06.05.2021 09:09