Syllabus

Titel
5711 Empirical Research and Analysis II
LV-Leiter/innen
Dipl.-Ing.Mag. Anita Zednik, Ph.D.
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
15.04.2019 bis 26.04.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Montag 29.04.2019 10:00 - 13:00 D5.1.001
Montag 06.05.2019 10:00 - 13:00 D5.0.002
Montag 13.05.2019 12:00 - 15:00 D5.0.002
Montag 20.05.2019 10:00 - 13:00 TC.5.15
Montag 27.05.2019 10:00 - 13:00 TC.5.03
Montag 03.06.2019 10:00 - 13:00 TC.4.03
Montag 17.06.2019 10:00 - 13:00 TC.3.01
Montag 24.06.2019 10:00 - 12:00 D5.1.001

Inhalte der LV

Data analysis is the basis of any evidence-based managerial decision-making. Data analysis is about recognizing patterns in data so that inferences about the real world can be made. The course teaches students about causal inference using selected methods of data creation, collection, and analysis. It draws on econometrics and statistical methods developed to estimate economic relationships, testing theoretical hypotheses and evaluating policies.

In particular, this course will cover the methods of laboratory and field experiments, specific approaches to establish causal relations with observational data, such as Instrumental Variables Regression, Differences-in-Differences Regression, Regression Discontinuities and Dynamic Causal Effects.

Lernergebnisse (Learning Outcomes)

On successful completion of the course, you should:

  • understand the concept of evidence-based decision-making.
  • be able to choose the right method of statistical data analysis to answer a research question;
  • have a good understanding of the discussed methods as well as their limitations;
  • understand the difference between causality and correlation;
  • be able to present and discuss findings from your research; 
  • perform simple analysis with STATA;

Regelung zur Anwesenheit

students are required to attend 80% of the lectures

Lehr-/Lerndesign

The data course is centered on specific problem-based example and case studies. Typically, we will start a topic with one or more examples and discuss how to find and/or generate data to answer these questions. This is followed by an introduction of the respective analysis method. In in-class tasks and homework assignments, students are asked to try out data generation and analysis themselves, with data provided to them.

Leistung(en) für eine Beurteilung

Oral presentations and participation in group discussions (20%)

Home work assignments (30%)
There will be homework assigned in nearly all lectures. Homework assignments can be done in groups and need to be submitted via email before the next lecture. Every class will start with a discussion of the homework.

Final exam (50%)
The final exam will cover the entire course.

Sonstiges

Resources available to students consist of lecture slides that will be provided online after class.

You are strongly encouraged to use STATA.
STATA is available on lab computers or you can purchase a student license.

Relevant chapters from textbooks will be announced in the lecture slides.

Zuletzt bearbeitet: 20.11.2018



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