ZEW summer courses are advanced PhD courses that will be offered as part of the elective courses in the CDSE course catalogue. They are open to CDSE and ZEW PhD students. They usually take place in July or August of each year and are given as a block course.

Course Registration Information

Registration is now open, please use the registration form (open from 14 April, 8 a.m.)

Final registration deadline is May 31, 2022. 

Course details will be provided in due course after the registration deadline.

ZEW Summer Courses 2022

LECTURER

Scott Cunningham, Baylor University

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits: Grades will be based on two activities.

  1. Four coding exercises (each 20%)
  2. Take home exam at the end (20%)

SCHEDULE

Date of the Course: July 5 – 8, 2022.

PREREQUISITES

All first year CDSE or equivalent courses.

COURSE CONTENT

Course Description: When researchers are not be able to field randomized experiments to study the causal effects of large social programs due to their size, associated costs, feasibility and ethical constraints, they often rely on natural experiments such as law changes or natural disasters.  The most popular research designs for estimating the causal effects using such natural experiments are the difference-in-differences design and synthetic control estimation. Both difference-in-differences and synthetic control have evolved considerably over the last several years, both in terms of econometric theory and software implementation. We will spend three days going through some of this new material reviewing both the intuition behind the statistical models and the technical details of the models themselves, while also gaining experience applying the methods to real data using R, python and/or Stata.

COMPETENCES ACQUIRED

  • an understanding of the design-based approach to causal identification using potential outcomes model, selection bias and the importance of randomization
  • experience with the major designs: Regression discontinuity, instrumental variables, difference in differences, synthetic control and if time matching
  • practice estimating causal effects using data, and examples of code R and Stata

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)

  • Cunningham (2021), Causal Inference: the Mixtape
  • Cunningham substack, Causal Inference: the Remix
  • Huntington-Klein (2022), The Effect, also available free.

LECTURER

Christoph Böhringer, University of Oldenburg

Course Type: elective

Credits: 5 ECTS 

Grading and assignment of ECTS credits: Seminar Paper

SCHEDULE

Date of the Course: July 11 – 15, 2022

PREREQUISITES

All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT

Topics in economic research / Advanced topics in sustainability economics Computer-based numerical simulations are an important method in applied economic analysis for assessing the impacts of policy reforms. This course provides an introduction to simple numerical models that can be used for quantifying the effects of important energy and climate policy regulations. Such regulations build either on market-based instruments (e.g., emission taxes and tradable permits) or command-and-control strategies (e.g., energy efficiency mandates, renewable portfolio standards, and technology phase-out policies).

COMPETENCES ACQUIRED

Apply computer-based numerical simulations to study the effects of important energy and climate policy regulations.

Examine the economic implications of political requirements (e.g. coal phase-out) or exogenous shocks (e.g. supply reductions for gas) for the electricity supply.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)

Will be provided prior to the course.

LECTURER

Christine Laudenbach, University of Bonn (Frankfurt)

Course Type: elective

Credits: 5 ECTS 

Grading and assignment of ECTS credits:

  1. Research proposal (seminar paper) (70%)
  2. Presentation (30%)

SCHEDULE

Date of the Course: July 25 – 29, 2022 

PREREQUISITES

All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT

The course gives an introduction to the field of household finance, which covers the use and management of financial instruments by individuals. Financial decisions are among the most important, but also the most complex problems private households face. The course will allow students to understand the main concepts, challenges, and tools of households’ financial management and enable them to critically assess the quality of financial products and services in the market. Topics include both savings behavior (including investment decisions and retirement planning) as well as credit decisions of consumers, highlighting several influencing variables such as experience, abilities and financial literacy, cultural background, and other personal characteristics. We will examine how households approach these important decisions, what mistakes they make and which solutions have been discovered and tested so far. Finally, the interaction of the consumer with his peers and financial intermediaries such as advisors will be discussed.

Methodologically, the course will discuss design considerations and various applications of (information provision) experiments in finance.

COMPETENCES ACQUIRED

The course aims are at giving students an overview of the research field of household finance both, with regards to current research topics and methodology used. After the course, students should be able to compare traditional and behavioral models in terms of their assumptions about human (financial) decision making. Further, students will learn how to develop a research question, and an experimental design to answer the question.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)

  • Barberis, Nicholas, and Richard Thaler (2003): “A survey of behavioral finance”, in Handbook of the Economics of Finance, Chapter 18, pp. 1053-1128
  • Campbell, J. Y. (2006). Household Finance. The Journal of Finance, 61(4), 1553-1604.
  • Beshears, J., J.J. Choi, Laibson, D., Madrian, B., (2018). Behavioral Household Finance, Handbook of Behavioral Economics
  • Gomez, F., M. Haliassos, T. Ramodarai, (2020). Household Finance, Journal of Economic Literature
  • Bergman, A., S. Hartzmark, A. Chinco, A.B. Sussman (2020), Survey Curious? Start-Up Guide and Best Practices For Running Surveys and Experiments Online, Working Paper.
  • Haaland, I., Roth, C., Wohlfart, J. (2021): Designing Information Provision Experiments, Journal of Economic Literature forthcoming.
  • Watch Daniel Kahneman (recipient of the Nobel Memorial Prize in Economic Sciences) on “Why We Make Bad Decisions About Money (And What We Can Do About It)”

LECTURER

Michael Lechner, Universität St. Gallen

Course Type: elective

Credits: 5 ECTS 

Grading and assignment of ECTS credits:

  1. Class participation (30%)
  2. Group project presentations (via Skype, about 2 weeks after the course)

SCHEDULE

Date of the Course: August 15 – 18, 2022 

PREREQUISITES

All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT

The course has three parts. In the first part, we discuss the use of machine and statistical learning methods for predicting outcomes. In the second part, we focus on the most popular causal research designs used in econometrics, like selection-on-observables, IV, regression-discontinuity and difference-in-difference. The third part concerns causal machine learning, i.e. how to combine the prediction methods of the machine learning literature with the causal research designs to obtain reliable causal inference in empirical studies.

COMPETENCES ACQUIRED

Students will obtain a basic knowledge of several popular machine/statistical learning methods, of the most important research designs, and how to combine both to obtain reliable and robust causal inference. They will be able to use these methods to conduct own empirical studies with causal machine learning methods.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)

Literature will be announced prior to the course.

LECTURER

Nicolas Schutz, Universität Mannheim

Course Type: elective

Credits: 5 ECTS 

Grading and assignment of ECTS-credits:

  1. Paper presentation (50%)
  2. Referee report (50%)

SCHEDULE

Date of the Course: Lectures: August 23 – 26, 2022

Students’ presentations: September 9, 2022

PREREQUISITES

All first year CDSE or equivalent courses. 

COURSE CONTENT

This course will cover simple microeconomic models that can be used to derive testable predictions, motivate empirical specifications, and explain empirical findings. We will cover a number of recent papers in industrial organization and international trade that have relied on combining applied-theoretical modelling with reduced-form empirical evidence. A reading list will be communicated at a later stage.

COMPETENCES ACQUIRED

Students are able to build simple micro models and use them as building blocks for empirical work.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)

Literature will be announced prior to the course.

Please see the CDSE course catalogue for courses offered by the CDSE during the Spring Term 2022.

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Contact

Researcher und Graduate Study Coordinator

Tel.: +49 (0)621 1235-398

Martin.Streng@zew.de