Professor MSO Dr. Arne Feddersen, University of Southern Denmark, Department of Environmental and Business Economics, Esbjerg, Denmark.
Prof. Dr. Oliver Budzinski, Ilmenau University of Technology, Department of Business and Economics, Chair for Economic Theory, Ilmenau, Germany.
Professor MSO Dr. Arne Feddersen, University of Southern Denmark, Department of Environ-mental and Business Economics, Esbjerg, Denmark.
Monday, August 19th to Friday, August 23rd, 2019.
Applied business economics research is one of the key fields in business economics and also related fields. Recently, PhD projects are – with an increasing frequency – designed as a paper-based (empirical) dissertation. The qualifications and skills obtained during master programs often hardly prepare students to write good scientific articles. Especially in the beginning of a PhD project with a strong empirical/quantitative focus, the challenges appear to be overwhelming. PhD students might have difficulties to cope, inter alia, with some of the following issues. (1) It might be difficult to translate the own research idea into an academic paper that can be submitted to a peer-reviewed journal. (2) Modern empirical research projects are more and more based on large data sets and call for skills with respect to data handling and management.
Furthermore, conducting empirical research on an academic level requires understanding of the philosophical and ethical background of empirical science (theory of science). Despite the increasing popularity of quantitative empirical research, these foundations of working empirically are often neglected. This includes the ability to reflect upon the prospects and limits of the own work as well as awareness of the responsibility of researchers for their results and their societal effects.
Students who completed the course will gain insight in the process of writing contemporary empirical articles and will learn about state-of-the-art data management that goes beyond simple spreadsheet computer applications like Excel. A good command of data management, in turn, is the key for the use of elaborate statistical techniques later in the process of the PhD project. The course will use the statistical software package Stata, which meets all these requirements and is widely used among applied researchers in business economics and related fields. Furthermore, students will be able to critically reflect upon the scientific value of their work as well as reflect upon their responsibility as a scientist towards society.
This PhD course is targeted for PhD students from business, economics, and other social sciences, who are planning or starting an empirical research project. Basic knowledge (master level) in statistics and econometrics is desirable but not necessary. Knowledge in Stata or other programming abilities are not preconditioned.
After completing the course, students will have:
- an understanding of problems associated with conducting contemporary empirical research projects.
- an understanding of state-of-the-art theories of empirical science.
- an ability to assess the prospects and limits of their empirical research in terms of its contribution to scientific progress.
- an understanding of the responsibility of the scientist for her results and the possible effects on society.
- an understanding of how to work with the Stata user interface.
- an ability to implement advanced data management in Stata.
- an ability to generate sophisticated graphics using Stata.
- an appreciation of the powerfulness of a script-based statistical software package like Stata, especially with respect to reproducible code for data management and graph generation.
- an ability to attend courses specialized on (specific) econometric techniques, which will use Stata, afterwards.
The following topics will be part of the course:
- Theories of empirical research
- Responsible science
- Introduction into the statistical software package Stata: user interface, program structure, help, user-written extensions (ado-files)
- Data import and export
- Data handling and data management
- Introduction to Stata graphics
- Descriptive analysis
- Do-file programming I: Introduction to functions, macros, and scalars
- Do-file programming II: prefixes, loops, lists, factor variables
- Brief introduction to regression analysis
- Baum, Christopher F. (2009). An Introduction to Stata Programming. College Station: Stata Press.
- Pfaff, Tobias (2009). A Brief Introduction to Stata with 50+ Basic Commands. Working Paper, Institute for Economic Education, University of Münster, Münster.
- Angrist, J. and J.-S. Pischke (2008). Mostly Harmless Econometrics. Princeton: Princeton University Press.
- Greene, William H. (2012). Econometric Analysis (7th ed.). Upper Saddle River: Prentice Hall.
- Chalmers, Alan F. (1990). Science and its Fabrication. University of Minnesota Press.
- Chalmers, Alan F. (1999). What Is This Thing Called Science? (3rd Edition). Open University Press.
- Kamarz, Francis, Joshua D. Angrist, David Blau, Armin Falk, Jean-Marc Robin, & Christopher R. Taber (2006). How to do empirical economics. Investigaciones Economicas, 30(2), 179–206.
- Karim Abadir, Karim & Jan R. Magnus (2002). Notation in Econometrics. Econometrics Journal, 5(1), 76–90.
- Bem, Daryl (2003). Writing the Empirical Journal Article. In John M. Darley, , Marc P. Zann., & Henry L. Roediger III (Eds.). The compleat academic: A practical guide for the beginning social scientist (2nd ed.). Washington D.C.: American Psychological Association.
The course has a lecture/discussion format and a hands-on “lab” component. The interactive lectures will focus on the theoretical background of empirical science as well as on the Stata user interface and programming. Hands-on “lab” exercises will use Stata and actual data sets to implement the data management tools from the lecture. Data sets will be provided for the hands-on “lab” component of the course, but it would also be possible to use own data sets. The hands-on “lab” component will take place in a computer lab on computers with Stata installed. Thus, participants are not expected to bring their own laptop.
Certificates of completion will be issued based on class attendance and participation, the submitted assignments, and an oral presentation.
Each student must submit a description (max. 2,500 words) of the empirical part of his/her PhD project. The description should include: (1) a short introduction; (2) (preliminary) research question(s); (3) a detailed description of the data or data collection process; (4) a detailed description of the planned empirical strategy (e.g. appropriate methods); and (5) key references. During the PhD course each student will be asked to present: (a) a short description of his/her research project; (b) the relation of the PhD project to existing research, the theoretical background, and the chosen or planned empirical method/strategy; (c) arguments why the proposed strategy/method is appropriate.
To apply to the course, please send an e-mail – no later than 04.08.2019 – to Arne Feddersen (email@example.com).
Do you have questions about the course? Please contact Oliver Budzinski (firstname.lastname@example.org) or Arne Feddersen (email@example.com).