Dear students,
Welcome to the mini-course Applied Econometrics for Spatial Economics. I hope you will enjoy the course. Please read this course manual very carefully.
I hope to see you all in class,
Hans Koster
1) Course structure and online teaching
This course will be taught entirely online. We will have sessions from 09:00 - 10:30 on Monday, Wednesday and Friday. In each session we will watch knowledge clips on Zoom (please click on this link). Make sure that you have downloaded Zoom and that you have created an account. The password to access those meetings is 750436 .
In these sessions there is then room to ask live questions (either via chat or camera). Furthermore, after each clip we will discuss applications and undertake exercises using Mentimeter in order for you to better understand the materials.
Attendance to the interactive sessions is highly recommended. If you attend, there is a Camera-On policy, implying that you always should put on your webcam. The sessions will not be recorded because of privacy issues, but the Mentimeter slides will be put on Canvas afterwards.
2) Course schedule
| Week 1 | Session 1 | Spatial econometrics I: Spatial data and variables | 11/23/20 |
| Session 2 | Spatial econometrics II: Spatial autocorrelation | 11/25/20 | |
| Session 3 | Spatial econometrics III: Spatial regressions | 11/27/20 | |
| Week 2 | Session 4 | Discrete choice I: Random utility framework | 11/30/20 |
| Session 5 | Discrete choice II: Binary choice models | 12/02/20 | |
| Session 6 | Discrete choice III: Multinomial choice models | 12/04/20 | |
| Week 3 | Session 7 | Identification I: Research designs | 12/07/20 |
| Session 8 | Identification II: RCTs / OLS/ IV | 12/09/20 | |
| Session 9 | Identification III: RDDs / Standard errors | 12/11/20 |
The accompanying literature and materials are downloadable from the 'modules' page.
3) Materials
Week 1 - Spatial Econometrics
Week 2 - Discrete Choice
Week 3 - Identification
4) Recapitulation materials
To refresh your knowledge on Ordinary Least Squares, Instrumental Variables and panel data (which I assume that you will master), please click here.