390045 SE PhD-M: Applying Advanced Regression Techniques (2024S)
Topics in Strategy and Innovation 3
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 12.02.2024 09:00 bis Mi 21.02.2024 12:00
- Abmeldung bis Do 14.03.2024 23:59
Details
max. 15 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine
03.04. 9:00-14:00
10.04. 9:00-14:00
24.04. 9:00-14:00
07.05. 11:00-16:00
22.05. 11:00-13:00
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Your final grade will be composed of the following elements:
20% class participation
30% presentation of a research article in class
50% final data-based project
20% class participation
30% presentation of a research article in class
50% final data-based project
Mindestanforderungen und Beurteilungsmaßstab
This course is meant for PhD Management students at the University of Vienna with an interest in doing empirical research.
Furthermore, we strongly recommend:
• Successful attendance of the course Econometrics, or in-depth knowledge of the contents of that course.
• Successful attendance of the course Multivariate Business Statistics, or in-depth knowledge of the contents of that course.
Furthermore, we strongly recommend:
• Successful attendance of the course Econometrics, or in-depth knowledge of the contents of that course.
• Successful attendance of the course Multivariate Business Statistics, or in-depth knowledge of the contents of that course.
Prüfungsstoff
One focus of this class will be on getting you to work on applied problems yourself. Essentially, the course will follow a “sandwich format” where front-end theory sessions, alternate with student presentations on selected research articles, and computer sessions during which we work on simulated and real data.
Literatur
Selected References:
• Foss, N. J., & Laursen, K. (2005). Performance pay, delegation and multitasking under uncertainty and innovativeness: An empirical investigation. Journal of Economic Behavior and Organization, 58, 2, 246-276.
• Gulati, R., & Singh, H. (1998). The Architecture of Cooperation: Managing Coordination Costs and Appropriation Concerns in Strategic Alliances. Administrative Science Quarterly, 43, 4, 781-814.
• Henkel, J. (2006). Selective revealing in open innovation processes: The case of embedded Linux. Research Policy, 35, 7, 953-969.
• Henkel, J., & Reitzig, M. (2008). Patent Sharks. Harvard Business Review, 86, 6, 129-133.
• Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge [Cambridgeshire: Cambridge University Press.
• Mukherjee, A. S., Lapre, M. A., & Van Wassenhove, L. N. (1998). Knowledge Driven Quality Improvement. Management Science, 44.
• Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, Mass: MIT Press.
• Wooldridge, J. M. (2002). Introductory econometrics: A modern approach. Princeton, N.J.
• Foss, N. J., & Laursen, K. (2005). Performance pay, delegation and multitasking under uncertainty and innovativeness: An empirical investigation. Journal of Economic Behavior and Organization, 58, 2, 246-276.
• Gulati, R., & Singh, H. (1998). The Architecture of Cooperation: Managing Coordination Costs and Appropriation Concerns in Strategic Alliances. Administrative Science Quarterly, 43, 4, 781-814.
• Henkel, J. (2006). Selective revealing in open innovation processes: The case of embedded Linux. Research Policy, 35, 7, 953-969.
• Henkel, J., & Reitzig, M. (2008). Patent Sharks. Harvard Business Review, 86, 6, 129-133.
• Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge [Cambridgeshire: Cambridge University Press.
• Mukherjee, A. S., Lapre, M. A., & Van Wassenhove, L. N. (1998). Knowledge Driven Quality Improvement. Management Science, 44.
• Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, Mass: MIT Press.
• Wooldridge, J. M. (2002). Introductory econometrics: A modern approach. Princeton, N.J.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 31.07.2024 12:06
1. It will introduce to more complex limited dependent variable problems
2. It will introduce to the analysis of panel data
3. It will introduce to programming with one of the most powerful software tools in econometrics, namely STATA.
This course seeks also to complement “Econometrics” for PhD students in another important way. Namely,
4. the course will offer you an additional opportunity to become ever more familiar with the “hands on” application of both basic and more advanced regression techniques for your own research purposes.
The focus of the course is “solid application”. Hence, neither our theory sessions nor any of the exercises will be centred on mathematical proofs but rather on a proper understanding of the logic, options, and caveats of the methods we discuss.
That said, experience shows that most of you will still benefit from a brief revision of the basics for a variety of reasons. We will therefore be starting from a very basic level to make sure that everybody is on board. This being said please note that we will be moving fast.
In order to minimize the overlap with the core courses on statistical methods, we will be revisiting basic econometrics only very briefly before we move on to more advanced techniques.