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, CDSB, 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 will open in April.

Final registration deadline is 24 May 2021.

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

ZEW Summer Courses 2021

LECTURER

Albrecht Glitz, Universitat Pompeu Fabra

Course Type: elective

Credits: 5 ECTS

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

  1. Students presentation.
  2. Students will submit a written “referee report”.

SCHEDULE

Date of the Course: July 5 – 9, 2021.

PREREQUISITES

All first year CDSE or equivalent courses.

COURSE CONTENT

This course provides an overview of the key concepts in the economics of migration. It is divided into two parts. The first part consists of 4 lectures in which we will cover the basic theoretical models. The second part will be seminar style. Each seminar will consist of an overview of the specific topic given by me, and presentations given by the students. For this purpose, students will team up and prepare a presentation about one of the key papers in the relevant literature. A list of possible topics for the presentations will be sent in advance (end of May).

Competences acquired:
In-depths knowledge of the various research themes in the economics of migration. Good understanding of appropriate empirical methods to identify causal relationships.

Further information (literature and recommended textbooks):
Literature: A detailed reading list will be announced prior to the course.

LECTURER
Alexander Rasch, Universität Düsseldorf

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS-credits: seminar paper

SCHEDULE
Date of the Course: Lectures: July 5 – 8, 2021

PREREQUISITES
All first year CDSE or equivalent courses.

COURSE CONTENT

  1. Introduction
  2. Definition and motivation
  3. Example industries
  4. Asymmetric information
  5. Reputation
  6. Second opinions
  7. Customer information
  8. Insurance
  9. Price regulation
  10. Quality competition
  11. Minimum quality requirements
  12. Restricted advertising
  13. Policy advice

Competences acquired:

  • Proper understanding of the economics of credence goods
  • Understanding the economic incentives of experts
  • Evaluation of policy-relevant issues in expert markets
  • Designing market institutions to overcome the information asymmetry inherent in credence goods
  • Knowledge of the methods used to investigate expert markets
  • Developing own research ideas

Further information (literature and recommended textbooks):
Detailed literature will be announced prior to the course.

LECTURER
Luigi Siciliani, University of York

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS-credits:

  • Paper presentation
  • referee report

SCHEDULE
Date of the Course: Lectures: July 19 – 23, 2021

PREREQUISITES
All first year CDSE or equivalent courses.

COURSE CONTENT
Content:

  • The effect of competition on hospital quality
  • The effect of quality and distance on patient choice of hospitals
  • Hospital mergers
  • DRG pricing and hospital incentives on quality and efficiency
  • Pay for performance
  • Public, private non-profit and private for-profit hospitals
  • Waiting times for hospital services

Methods:

About 20% of the lecture material will develop (simple) theoretical models that can be used to guide the empirical analysis on the topics above.

The remaining 80% will be used to illustrate applied micro econometric methods, with a focus on causality, that can inform key policy issues related to the topics above. Empirical methods will cover difference-in-difference methods, instrumental variables, matching, regression discontinuity.

Competences acquired:
Upon completing the module, the student will: have acquired competences on formulating a research question within the broad area of the economics of hospitals or health care providers; have learnt different identification strategies in this research area; have a clear idea of the data required, e.g. in relation to quality, to address a research question in this area; be able to formulate a relatively simple theoretical model which supports the empirical analysis.

Further information (literature and recommended textbooks):
A detailed reading list based on journal articles will be provided for each topic.

LECTURER
Nicolas Schutz, Universität Mannheim

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS-credits: Paper presentation (50%), referee report (50%)

SCHEDULE

Date of the Course: Lectures: July 26 – 30, 2021
Students’ presentations: First or second Friday of September 2021

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.

LECTURER
Michael Lechner, Universität St. Gallen

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:
Class participation (30%)
Group project presentations (via Skype, about 2 weeks after the course)

SCHEDULE
Date of the Course: August 9 – 14, 2021

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
Grant McDermott, University of Oregon

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:

  • Student presentation
  • Student paper

SCHEDULE
Date of the Course: August 23 – 27, 2021

PREREQUISITES
All first year CDSE or equivalent courses.

COURSE CONTENT

This seminar is targeted at economics PhD students and will introduce you to the modern data science toolkit. While some material will likely overlap with your other quantitative and empirical methods courses, this is not just another econometrics course. Rather, my goal is bring you up to speed on the practical tools and techniques that I feel will most benefit your dissertation work and future research career. This includes seemingly mundane skills, generally excluded from the core graduate curriculum, which are nevertheless essential to any scientific project. We will cover topics like version control (Git) and project management; data acquisition, cleaning and visualization; efficient programming; and tools for big data analysis (e.g. relational databases and cloud computation). In short, we will cover things that I wish someone had taught me when I was starting out in graduate school.

Competences acquired:
Familiarity / competence with some of the cornerstones of the modern data science toolkit (GitHub, unix shell, functional and parallel programming in R, Docker, Cloud computation and Databases). Time permitting, we’ll also cover topics like geospatial analysis and machine learning.

Further information (literature and recommended textbooks):
There is no prescribed textbook for this course. Instead, we will be working off the lecture material and notebooks that I have developed here.

LECTURER
David Jaeger, University of St. Andrews

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:
research proposal (8 pages)

SCHEDULE
Date of the course: August (exact date tbd), 2021

PREREQUISITES
All first year CDSE or equivalent courses.

COURSE CONTENT

This is an introduction to some of the ways that econometricians think about identifying causal effects in observational (i.e. non-experimental) data. We will examine several of the standard ways of estimating causal effects in the presence of potentially unobserved confounding factors. We will also discuss how to make proper statistical inferences about those estimates.  Both theoretical and applied work will be examined.

Competences acquired:
Familiarity with econometric methods used for causal inference.

  • Ability to evaluate and critique existing empirical research that uses causal inference methods.
  • Identify and apply appropriate causal inference methods in students’ own research.

Further information (literature and recommended textbooks):
The primary books for the class are Mostly Harmless Econometrics by Joshua Angrist and Jörn-Steffen Pischke (Princeton University Press, 2008), denoted by AP in the list of readings; and Microeconometrics:  Methods and Applications by Colin Cameron and Pravin K. Trivedi (Cambridge University Press, 2005), denoted by CT on the reading list.  Other readings will be available on Zotero.

Please see the CDSE course catalogue for courses offered by the CDSE during the Spring Term 2021.

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