Login for PhD students/staff at UCPH
Login for others
Home
Course Catalougue Science
Department of Science Education
Fundamentals of the PhD education at SCIENCE
Responsible Conduct of Research
Specialised course
Toolbox course
Course and cancellation fees for PhD courses
How to log on to the course system and how to apply for a course
How to manage your course enrollments
How to log in as course provider
Contact information
Processing...
Advanced Applied Econometrics: Causal Inference with Observational Data
Provider: Faculty of Science
Activity no.: 5226-18-03-31
Enrollment deadline: 07/05/2018
Date and time
14.05.2018, at: 09:00 - 18.05.2018, at: 16:00
Regular seats
50
Course fee
1,000.00 kr.
Lecturers
Arne Henningsen
Contact person
Charlotte Bukdahl Jacobsen E-mail address: cja@ifro.ku.dk
Enrolment Handling/Course Organiser
Arne Henningsen E-mail address: arne@ifro.ku.dk
Written language
English
Teaching language
English
Semester/Block
Spring
Exam form
Written assignment
Grading scale
Passed / Not passed
Course workload
Course workload category
Hours
Lectures
25.00
Exercise(s)
10.00
Preparation / Self-Study
25.00
Exam
70.00
Evaluation
1.00
Sum
131.00
Content
Social science researchers are usually interested in investigating causal relationships. The analysis
of causal relationships is generally easiest based on experimental data. The use of experiments in
social sciences, however, has many limitations, and most empirical studies are based on
observational (i.e., nonexperimental) data. The participants of this course will learn the theory and
practice of state-of-the-art empirical approaches used for investigating causal relationships with
observational data. The course participants will also learn how to evaluate and discuss the
appropriateness of identification strategies for analysing causal relationships and to choose the
most appropriate identification strategy for analysing a specific research question. All this will help
the participants obtain more credible and reliable results in their empirical work and to publish their
work in better journals.
Learning outcome
After having completed the course, the course participants will know state-of-the-art statistical and
econometric methods for causal inference with observational data, and they will be able to apply
these methods to investigate causal relationships based on observational data. The participants
will also be able to evaluate and discuss the appropriateness of identification strategies and they
will be able to choose an appropriate identification strategy for a specific research question.
Literature
- Bellemare, Marc F. (2018), The Craft of Econometrics , unpublished lecture notes.
- Angrist, J.D. and Pischke, J.S. (2009), Mostly Harmless Econometrics: An Empiricist's Companion. Princeton university press.
- Excerpts from various econometrics textbooks.
- Various articles in scientific journals.
Teaching and learning methods
The theory will be taught mostly through (interactive) lectures, dialogue teaching, and self-study.
The practice will be taught mostly through exercises, group work, self-study, and the exam (i.e.,
conducting econometric/statistical analyses and writing a report, see following section).
Exam form and criteria for assessment:
The exam consists of a brief report about a statistical or econometric analysis (maximum five
pages) each participant has to write and send to the course organiser no later than two months
after the end of the course. The participants need to find a suitable research question and data set
for their analyses themselves. The research question needs to be within the social sciences and
needs to address a causal relationship. The data set needs to contain observational (i.e.,
nonexperimental) data. It is encouraged that the research question and data set are parts of the
participants’ PhD projects. In their reports, the participants need to state their research question,
describe their data, methodology, model specification and results, discuss the appropriateness of
their identification strategy, and derive conclusions based on their empirical analysis. A participant
passes the exam if his/her report indicates that he/she has obtained the intended learning outcome
(see section “learning outcome”).
Preparation and self-study:
Participants are expected to prepare for the course by reading the course material (mostly extracts
from textbooks and journal articles) that will be sent to the students about 4 weeks before the
course starts. It is also expected that the participants recapitulate the contents of the course by
reviewing the lecture notes and the other course material in order to appropriately conduct the
analyses and write their report for the exam.
DATE/TIME/ROOM/ADDRESS
Monday 14 May at 8-13 in room A1-04.01, Grønnegårdsvej 7. From 13-17 in room A2-70.01, Thorvaldsensvej 40
Tuesday 15 May at 8-13 in room A1-01.12, Bülowsvej 17. From 13-17 in room A1-01.14, Bülowsvej 17
Wednesday 16 May at 8-17 in room A2-84.-11, Thorvaldsensvej 40
Thursday 17 May at 8-13 in room A2-82.01, Thorvaldsensvej 40. From 13-17 in room A2-70.02, Thorvaldsensvej 40
Friday 18 May at 8-12 in room A2-82-01, Thorvaldsensvej 40. From 12-17 in room A2-70.01, Thorvaldsensvej 40
Lecturers
- Marc F. Bellemare (Department of Applied Economics, University of Minnesota)
- Arne Henningsen (Department of Food and Resource Economics, University of Copenhagen)
Remarks
Practicalities:
Coffee, tea, and lunch is provided at each of the five teaching days. Dinner is provided at one of
the teaching days. Participants need to find accommodation themselves.
Search
Click the search button to search Courses.
[Alle udbydere]
Science
Choose course area
Course Catalougue Science
Choose sub area
Course calendar
See which courses you can attend and when
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Processing...
RadEditor - HTML WYSIWYG Editor. MS Word-like content editing experience thanks to a rich set of formatting tools, dropdowns, dialogs, system modules and built-in spell-check.
RadEditor's components - toolbar, content area, modes and modules
Toolbar's wrapper
Paragraph Style
Font Name
Real font size
Apply CSS Class
Custom Links
Zoom
Content area wrapper
RadEditor hidden textarea
RadEditor's bottom area: Design, Html and Preview modes, Statistics module and resize handle.
It contains RadEditor's Modes/views (HTML, Design and Preview), Statistics and Resizer
Editor Mode buttons
Statistics module
Editor resizer
Design
HTML
Preview
RadEditor - please enable JavaScript to use the rich text editor.
RadEditor's Modules - special tools used to provide extra information such as Tag Inspector, Real Time HTML Viewer, Tag Properties and other.