4663 Business Research Methods
Dr. Ali Özkes
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
25.02.2019 bis 04.03.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Wochentag Datum Uhrzeit Raum
Donnerstag 07.03.2019 12:30 - 15:00 D4.0.019
Donnerstag 14.03.2019 12:30 - 15:00 D4.0.019
Donnerstag 28.03.2019 12:30 - 15:00 D5.1.004
Donnerstag 04.04.2019 12:30 - 15:00 D5.1.004
Donnerstag 11.04.2019 12:30 - 15:00 D4.0.019
Donnerstag 02.05.2019 12:30 - 15:00 D5.1.004
Donnerstag 09.05.2019 12:30 - 15:00 D4.0.019
Donnerstag 06.06.2019 12:30 - 15:00 D5.1.004
Freitag 14.06.2019 09:30 - 12:30 D5.0.001

Inhalte der LV

This course will provide an introduction on how to analyse and understand different types of data in economics and associated disciplines, focusing in particular on the estimation of causal relationships. It will cover applied techniques that are applicable to a wide array of real problems related to business and public policy. The course emphasises empirical applications.

The course will discuss randomised trials, regression with a single regressor, multiple regression, the basics of functional form analysis, and the evaluation of regression studies. It then proceeds to cover regression with panel data, regression with a limited dependent variable, and instrumental variables methods. The course concludes with experiments and quasi-experiments, topics that provide an opportunity to return to questions of estimating causal effects.

The course will also focus on developing the skill to apply econometric and statistical methods using computer packages. At the end of the course, students should be able to go through the multiple stages of empirical research: searching for interesting questions, devising an appropriate research design, collecting the data, and implementing the analysis.


Lernergebnisse (Learning Outcomes)

On successful completion of the course, you should be able to:

- demonstrate an understanding of different types and sources of data, statistical concepts, and empirical methods,

- understand how to solve quantitative questions with quantitative answers,

- understand the difference between empirical correlation and causation,

- understand how to conduct and critique empirical studies,

- demonstrate the ability to estimate different regression models, interpret the results, perform relevant diagnostic tests, and derive conclusions,

- demonstrate the ability to use statistical computer software, namely Stata, to manage and analyse research data.

Regelung zur Anwesenheit

We expect students to attend all meetings of this course.


Lectures will focus on a real problem/application relating to economic, business, and public policy issues. Students will then be shown what data sources and types are required to answer empirical questions of interest; typically relating to individual and firm behaviour, business decision-making, and social policy interventions. Once the research questions are established, examples of necessary empirical data and econometric tools will be introduced and discussed. To link the statistical and econometric concepts to practical examples, students will be shown how to manage and analyse data during class using the statistical software package Stata.

Each lecture will typically introduce one type of empirical analysis technique, along with one or more applications. In order to get a better understanding of different research methods, students will be asked to complete problem sets (assignments) using actual data sets. These problem sets will be computer-based and will enable students to learn how to manage and analyse data, as well as estimate and critically interpret empirical results and findings. Discussion of these problem sets and other examples will take place in class, mainly during the second-half of each lecture.

Leistung(en) für eine Beurteilung

Class Participation: 10%

Problem Sets: 40%

Final Exam: 50%


The required textbooks are:

(1) J.H. Stock and M.W. Watson, Introduction to Econometrics (third edition), Addison-Wesley, 2011. Earlier editions of this textbook are also fine, but may differ slightly in some parts of the content.

(2) Angrist, J. D., and Pischke, J. S. (2014). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

Zuletzt bearbeitet: 16.01.2019