Dear participants
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 face-to-face, with the option to access the lectures online, although in-person attendance is strongly preferred. We will have sessions of 60 minutes each. During each session there is room to ask questions. Furthermore, we will discuss applications and undertake exercises using Mentimeter in order for you to better understand the materials.
2) Course schedule
| Wednesday | ||
| 09:30-10:30 | Lecture 1 | Discrete Choice I (The random utility framework) |
| 10:45-11:45 | Lecture 2 | Discrete Choice II (Estimating discrete choice models) |
| 12:00-13:00 | Lecture 3 | Spatial Econometrics I (Spatial data) |
| 14:00-15:30 | Tutorial 1 | Introduction to Assignment 1 & working on Assignment 1 |
| Thursday | ||
| 09:30-10:30 | Lecture 4 | Spatial Econometrics II (Spatial autocorrelation) |
| 10:45-11:45 | Lecture 5 | Spatial Econometrics III (Spatial regressions) |
| 12:00-12:30 | Lecture 6 | Identification I (Research design) |
| 13:30-14:00 | Tutorial 2 | Discussion of Assignment 1 |
| 14:00-15:00 | Tutorial 3 | Introduction to Assignment 2 & working on Assignment 2 |
| Friday | ||
| 09:30-10:00 | Lecture 7 | Identification II (RCTs, OLS, IV, quasi-experiments) |
| 10:00-10:30 | Lecture 8 | Hedonic pricing I (Theory) |
| 10:45-11:45 | Lecture 9 | Hedonic pricing II (Estimation) |
| 12:00-12:30 | Tutorial 4 | Discussion of Assignment 2 |
The accompanying literature and materials are downloadable from the 'materials' pages below.
3) Materials
Topic 1 - Spatial Econometrics
Topic 2 - Discrete Choice
Topic 3 - Identification
Topic 4 - Hedonic pricing
Topic 5 - Quantitative spatial economics (not covered in current lecture series)
4) Assignments
See here.
5) Recapitulation materials
To refresh your knowledge of Ordinary Least Squares, Instrumental Variables and panel data (which I assume that you will master), please click here.