Course Descriptions

Program: Doctoral Program Module: Economic Analysis & Policy Course: Economic Analysis & Policy Course No.: PhD-1

Credits: 5

Lecturer: Prof. Pierfrancesco La Mura, Ekaterina Demidova Learning objectives This is a Ph.D.-level course in Microeconomics, which shall particularly emphasize the strategic roles of various actors (individuals, corporations...) and outcomes in a variety of economic settings (market and non-market situations). Content

Specifically, the course is structured as follows: after the introduction to utility theory and the theory of decisions under uncertainty, we shall introduce the game-theoretical approach to modelling economic situations. Next, we shall look at two fields of application for our framework of particular business interests: the theory of auctions and mechanism design (including negotiation protocols) and the economics of information and incentives in organisations.

Teaching Methods

The course relies on lectures and group assignments to convey the material. Active participation and discussions are encouraged.

Conditions of participation

Admission to the HHL Doctoral Program.

Application, combination and frequency

The course is a core course. The course is held once a year. Students are free to choose if they want to attend the course “Economic Analysis and Policy” or the course “Corporate Governance”.

Conditions for Credit Points and grades

Credit Points are awarded for passing the module “Economic Analysis & Policy”. The module is passed if the weighted average of the single grades of the courses is 4.0 or better. The grade of the course is determined by the weighted average of the single grades of the examinations. The course contains the following examinations: group assignment (20 %) and final exam (80 %).

 

The grades range from 1 to 5. Workload

The course “Economic Analysis & Policy” accounts for 5 ECTS, which are equivalent to a total workload of 150 hours.

Duration

The course extends over one term.

Literature

Program: Doctoral Program Course: Philosophical Underpinnings of Economic and Management Research Course No.: PhD-2

Credits: 5

Lecturer: Prof. Dr. Andreas Suchanek

Learning objectives and outcomes

The aim of the course is to develop a deepened understanding of the methodological as well as ethical presuppositions of economic theories and their application by studying and discussing classical texts from social philosophy and methodology of science. As a by-product the course aims at conveying some basic insights about criteria for a good dissertation.

Content

The course covers core issues of the philosophy and methodology of social science such as the problem of social order, the role of institutions and their legitimacy, the ethical quality of a market economy, the role and content of ethics education in management.

Teaching methods

The course is held as a seminar. Groups will have to present the main ideas of a classical author (T. Hobbes, A. Smith, K. Marx et al.); the participants are requested to read the announced texts in advance and to take part in the discussions.




The course is a core course and is held once a year.

Conditions for credit points and grades

Credit Points are awarded for passing the core module “Philosophical Underpinnings of Economic and Management Research”. The module is passed if the weighted average of the single grades of the courses is 4.0 or better. The grade of the course is determined by the weighted average of the single grades of the examinations. The course contains the following examinations:  Group presentation (100 %) The grades range from 1 to 5.

Workload

The course “Philosophical Underpinnings of Economic and Management Research” accounts for 5 ECTS, which are equivalent to a total workload of 150 hours.

Duration


Literature

Will be announced via Email in the second half of September.

Program: Doctoral Program Module: Corporate Governance Course: Corporate Governance Course No.: PhD-3

Credits: 5

Lecturer: Prof. Dr. Michael Wolff, Prof. Dr. Marc Steffen Rapp Learning objectives This is a Ph.D.-level course. Corporate Governance is an increasingly important issue of economic research. This course will offer you a profound and outcomes introduction to the most relevant aspects. Content

The course aims to familiarize students with the foundations and recent trends in corporate governance research. Thereby, the course adopts the finance view of corporate governance following Shleifer & Vishny (1997, JoF): “Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment.“ Accordingly, the following questions will be discussed:  How do the suppliers of finance get managers to return some of the profits to them?  How do they make sure that managers do not steal the capital they supply or invest it in bad projects?  How do suppliers of finance control managers?

Teaching Methods

The course relies on lectures and group assignments to convey the material. Active participation and discussions are encouraged.




The course is a core course. The course is held once a year. Students are free to choose if they want to attend the course “Corporate Governance” or the course “Economic Analysis and Policy”. Due to the seminar style of the course, the number of students is limited to 15 students.


Credit Points are awarded for passing the module “Corporate Governance”. The module is passed if the weighted average of the single grades of the courses is 4.0 or better. The grade of the course is determined by the weighted average of the single grades of the examinations. The course contains the following examinations:  two initial reports (33,3 %) To ensure a proper preparation, you are asked to hand-in two referee reports (4-pages of text each) on i) a theoretical paper and ii) an empirical paper discussing general topics of corporate governance research prior to the course (e.g., Tirole, 2001, Econometrica or Börsch-Supan / Köke, 2002, German Economic Review) Guidelines on “how to write a referee report” will be provided. 

in-class presentation (33,3 %) and

You are asked to present an individually assigned paper within class. 

final exam (33,3 %). The grades range from 1 to 5.

Workload

The course “Corporate Governance” accounts for 5 ECTS, which are equivalent to a total workload of 150 hours.

Duration


Literature

Dealing with theoretical issues of CG  An Economic Overview of Corporate Institutions (e.g., Tirole, 2006, Part I, Princeton)  Foundations of Corporate Governance (Shleifer/Vishny, 1997; Tirole, 2001)  Security Design (e.g., Tirole, 2006, Allen/Gale, 1988; Part IV; Noe et al., 2006; Burkart / Lee, 2008; Adams/ Ferreira, 2008)  Recent Trends in Corporate Governance Research (e.g., Hermalin, 2005) Dealing with problems of empirical CG research  Preliminaries and Regression Design (Angrist/Pischke, 2009)  Endogeneity in Corporate Governance Research (e.g., Angrist/Pischke, 2009; Börsch-Supan / Köke, 2002; Larcker / Rusticus, 2007)  Selected Empirical Research in Corporate Governance (to be announced) Further literature and references will be distributed prior to and in class.

Program: Doctoral Program Course: Introduction to Econometrics Course No.: PhD-13

Credits: 2,5

Lecturer: Dr. Oliver Hoßfeld Learning objectives and outcomes

The aim of this course is to provide a thorough understanding of regression analysis and related statistical concepts. By the end of the course, students should understand the statistical theory underlying regression and be familiar with a number of applications in areas such as finance, marketing and management.

Content

Regression Analysis: OLS Estimation, Hypothesis Testing, Functional and Stochastic Specification

Teaching Methods Lectures and exercises Conditions of participation

Admission to the HHL Doctoral Program. In order to allow self study, the students receive comprehensive reading material and additional references in advance.


The course is an elective course. Additionally, 3 other elective courses have to be chosen. The course is held once a year.


Credit Points are awarded for passing the course. The course is passed if the grade of the course is 4,0 or better. The course contains the following examination: home assignment. The grades range from 1 to 5.

Workload

The course “Introduction to Econometrics” accounts for 2,5 ECTS, which are equivalent to a total workload of 75 hours.

Duration


Literature

 A.H. Studenmund, Using Econometrics: A Practical Guide, 2011, 6th edition (recommended)  J.M. Wooldridge, Introductory Econometrics, 2009, 4th ed.

Program: Doctoral Program Course: Methods of Multivariate Statistics Course No.: PhD-11

Credits: 2,5

Lecturer: N.N. Learning objectives and outcomes

The objective of the course is to give you an understanding of various methods of multivariate statistics, understand in which circumstances a certain method is applicable, and gain practical experience in applying the methods to various data sets (from economics and business) using the software SPSS.

Content

1) 2) 3) 4) 5)

Factor Analysis Multivariate Analysis of Variance Cluster Analysis Linear Discriminant Analysis Canonical Correlation Analysis

Teaching Methods Lectures and SPSS Tutorials Conditions of participation

Admission to the HHL Doctoral Program. Prerequisites: It is recommended that the course “Introduction to Econometrics” should be taken prior to attending this course. Participants should be familiar with the concepts of univariate statistics.




Credit Points are awarded for passing the course. The course is passed if the grade of the course is 4,0 or better. The grade of the course is determined by the weighted average of the single grades of the examinations. Examination: Home Assignment The grades range from 1 to 5.

Workload

The course “Methods of Multivariate Statistics” accounts for 2,5 ECTS, which are equivalent to a total workload of 75 hours.

Duration


Literature

The general references listed below cover the material of the course. Most of these books will cover all of the topics of the course. It should be noted that the presentation of statistical methods in different textbooks differs mainly in terms of depth and flavor of explanation. So there is no need to look at multiple references, once you have found one or two that suit your needs and taste. A more specific reading list will be circulated closer to the course. • • •

K. Backhaus, B. Erichson, W. Plinke, R. Weiber: Multivariate Analysemethoden (13th edn.). Springer-Verlag, Berlin, Heidelberg, 2010. J.F. Hair (Jr.), W.C. Black, B.J. Babin, R.E. Anderson, R.L. Tatham: Multivariate Data Analyis (6th edn.). Pearson, Prentice Hall, Upper Saddle River, NJ, 2006. J. Hartung, B. Elpelt: Multivariate Statistik (7th edn.). Oldenburg Wissen-schaftsverlag, München, 2007.

• •

S. Sharma: Applied Multivariate Techniques. John Wiley & Sons, 1996. B.G. Tabachnick, L.S. Fidell: Using Multivariate Statistics (5th edn.). Pearson Education, 2007

Mathematically and statistically more demanding is the following reference: •

L. Fahrmeir, A. Hamerle, G. Tutz (eds.): Multivariate statistische Verfahren (2nd edn.). Walter de Gruyter, Berlin, 1996.

Program: Doctoral Program Course: Structural Equation Modeling Course No.: PhD-8

Credits: 2,5

Lecturer: Prof. Dr. Jörg Henseler Learning objectives and outcomes

Structural equation modeling is a more advanced technique of multivariate statistics that allows latent (that is, unobserved) variables and that is used to empirically validate theoretically developed causal models in the social sciences disciplines. There are two approaches to structural equation modeling: a covariance based approach and a variance based approach, namely partial least squares (PLS) path modeling. The covariance based approach is included in the SPSS software AMOS, and partial least squares path modeling is provided by the software SmartPLS. The objective of the course is to give you an introduction to both the covariance and the variance based approach to structural equation modeling and to introduce you to the corresponding software packages AMOS and SmartPLS.

Content

1) Fundamentals of Structural Equation Modeling 2) Introduction to the Covariance Based Approach and the SPSS Software AMOS 3) Introduction to the Variance Based Approach Partial Least Squares Path Modeling and the Software SmartPLS

Teaching Methods  Lectures  Tutorials Conditions of participation





Credit Points are awarded for passing the course. The course is passed if the grade of the course is 4,0 or better. The grade of the course is determined by the weighted average of the single grades of the examinations.

Prerequisites: Participation in the courses “Introduction to Econometrics” and “Methods of Multivariate Statistics” prior to this course is required.

Examination: Home Assignment The grades range from 1 to 5. Workload

The course “Structural Equation Modeling” accounts for 2,5 ECTS, which are equivalent to a total workload of 75 hours.

Duration


Literature

The general references listed below cover the material of the course. A more specific and selective reading list will be circulated closer to the course.  

K. Backhaus, B. Erichson, R. Weiber: Fortgeschrittene Multivariate Analysemethoden. Springer-Verlag, 2010. F. Bliemel, A. Eggert, G. Fassott, J. Henseler: Handbuch PLS-Pfadmo-

  

dellierung - Methoden, Anwendung, Praxisbeispiele. Schäffer-Poeschel, 2005. K.A. Bollen: Structural Equations with Latent Variables. John Wiley & Sons, 1989. V. Esposito Vinzi, W.W. Chin, J. Henseler, H. Wang (eds.): Handbook of Partial Least Squares – Concepts, Methods and Applications. SpringerVerlag, Berlin, Heidelberg, 2010. R. Weiber, D. Mühlhaus: Strukturgleichungsmodelle – Eine anwendungs-orientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS and SPSS. Springer-Verlag, Berlin, Heidelberg, 2010.

Mathematically and statistically more demanding is the following reference: 

L. Fahrmeir, A. Hamerle, G. Tutz (eds.): Multivariate statistische Verfahren (2nd edn.). Walter de Gruyter, Berlin, 1996

Program:

Doctoral Program

Course:

Time Series Analysis

Course No.: PhD-14 Lecturer:

Credits: 2,5

Dr. Oliver Hoßfeld

Learning objectives and outcomes

The aim of this course is to provide a basic understanding of applied univariate time series analysis. You will learn how to model and forecast single sequences of data (univariate time series) with the help of several stochastic processes (AR, MA, and ARMA) and see various examples of practical business forecasting (such as sales forecasting). You will be able to model volatility (ARCH and GARCH), know why it is important whether time series are stationary or not and how to avoid the risk of running “spurious regressions”. The focus of the course is on providing an intuitive understanding of the above mentioned models, not on rigorous mathematical derivations. You will see various practical examples in fields such as finance, marketing and economics and get hands-on-experience in Eviews.

Content

 Introduction to time series analysis  Univariate time series models o Stochastic difference equation models o ARMA models o Autocorrelation and partial autocorrelation function o Box-Jenkins model selection (w/ and w/o seasonality) o ARCH and GARCH models

Teaching Methods Lectures and exercises Conditions of participation

Admission to the HHL Doctoral Program. In order to allow self study, the students receive comprehensive reading material and additional references in advance. Prior econometric knowledge at the level of "Introduction to Econometrics"




Credit Points are awarded for passing the course. The course is passed if the grade of the course is 4,0 or better. The course contains the following examination: home assignment. The grades range from 1 to 5.

Workload

The course “Univariate Time Series Analysis” accounts for 2,5 ECTS, which are equivalent to a total workload of 75 hours.

Duration


Literature

Gujarati, D.N., Porter, C.N., Basic Econometrics (2009), McGraw-Hill, chapter 21 and 22 (required reading) Selected chapters from: Enders, W., Applied Econometric Time Series (2004), Wiley (recommended) Heij, C., de Boer, P., Franses, P.H., Kloek, T., van Dijk, H.K., Econometric Methods with Applications in Business and Economics (2004), Oxford University Press (recommended)

Evans, M.K., Practical Business Forecasting (2003), Wiley-Blackwell Note: Lectures will be based on various econometrics books (most notably, the ones above). As most books in this field are fairly technical, I suggest not to read these prior to the course (with the exception of Gujarati and Porter (chapters 21+22)). It might be quite frustrating at first unless you are mathematically well-trained.

Program: Doctoral Program Course: Management Research Methods: a qualitative research approach Course No.: PhD-7

Credits: 2,5

Lecturer: PD Dr. Anne-Katrin Neyer Goals and methods

The course is designed for PhD-students, who are interested in qualitative research. The goal is to provide, discuss and structure qualitative research methods with regard to the specific research projects the students are working on. In the course the participants will therefore prepare a presentation with regard to the methodological approach of their PhD project. In doing so, they will receive customized feedback to their respective research project, which will help them to work on their “take-home exam”, in which they will describe the methodological approach of their PhD project.

Content

First, the students will be provided with the basis research tools and instruments. Afterwards it will be discussed how one can derive to his/her research questions as well as the research topics: What is the intended contribution, and: to which research community would you like to contribute? Providing the students with insights for conceptualization and for the development of their own research design is an essential component of this course. Distinct approaches of how to conduct qualitative research will be provided within this course.

Teaching Methods Presentations, interactions, discussions Conditions of participation





Credit Points are awarded for passing the course. The course is passed if the grade of the course is 4,0 or better. The course contains the following examinations:  Take Home Exam (100%) The grades range from 1 to 5.

Workload

The course “Methods of Management Research” accounts for 2,5 ECTS, which are equivalent to a total workload of 75 hours.

Duration


Literature

 Bartunek, J., Rynes, S., & Ireland, R. D. 2006. What makes management research interesting, and why does it matter? Academy of Management Journal, 49: 9-15.  Burton-Jones, A. 2009. Minimising method bias through programmatic research. MIS Quarterly, 33: 445-471.  Eisenhardt, K. 1989. Building theories from case research. Academy of Management Review, 14: 532-550.  Gephart Jr., R. P. 2004. Qualitative research and the Academy of Management Journal? Academy of Management Journal, 47: 454-462.  Huff, A. S. 2009. Designing Research for Publication. Thousand Oaks, CA:

Sage.  Lee, T., Mitchell, T., & Sablynski, C. 1999. Qualitative research in organizational and vocational psychology, 1979 - 1999. Journal of Vocational Behavior, 55: 161-187.  Pratt, M. G. 2009. For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52: 856-862.

Course Descriptions - HHL

Course Descriptions - HHL

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