Spatial Econometrics

Team project using R

Author

Affiliation

Roger Bivand

Norwegian School of Economics

Published

May 10, 2024

Course description

Planned topics

Introducing Spatial Econometrics
Spatial data
Working with spatial data
Creating spatial weights objects
Spatial Econometrics: Antecedents
Economic and social research questions using spatial data
Tests of spatial autocorrelation, model specification
Spatial autoregressive models: conditional (CAR) and simultaneous (SAR)
Estimation of spatial autoregressive models: methods (GMM, ML, Bayesian)
No orthogonality between regression and autoregression coefficients
Prediction and spatial models
Eigenvectors and eigenvalues of graphs of relationships between cross-sectional observations

Team projects

The teams should aim to prepare notebooks showing reasoning, code, workflow, conclusions, and should include a log of team discussions, and the conclusions should show how the team, with hindsight, might have modified the topics and workflow had more time, data, or other resources been available.

Teams should try to limit joint team work to the 30 hours available, but team members may choose to make progress individually to tackle technical snags. The team may choose to delegate parts to team members; use of GitHub or similar to share writing of the notebook is encouraged.

establish communication through moodle for on-demand consultations in or outside the scheduled project hours
week 11: draft team project proposals to be decided
week 12: feedback on progress with projects
week 15: mid-term team project reports
week 16: feedback on progress with projects
week 17: feedback on progress with projects
Monday 6 May, 15:15-18:00 presentation of team projects and wrap-up

Online resources

Pebesma and Bivand (2023) is also available at: https://r-spatial.org/book
More on constructing spatial neighbour objects: https://r-spatial.github.io/spdep/articles/nb_sf.html
Spatial weights as graphs: https://r-spatial.github.io/spatialreg/articles/nb_igraph.html
EMOS talk 19 April 2023: https://rsbivand.github.io/emos_talk_2304/, https://www.youtube.com/watch?v=spYPyHxa7v8
UAM short courses: https://rsbivand.github.io/UAM21_I/, https://rsbivand.github.io/UAM21_II/
WhyR 2020 keynote: https://www.youtube.com/watch?v=mEAyQ8bv1zU
2020 BAN 422 Masters course Visualization: https://rsbivand.github.io/BAN422_V20/
ECS530 PhD course 2019 (with recorded talks): https://rsbivand.github.io/ECS530_h19/, https://www.youtube.com/watch?v=KkIbg50Pa0I&list=PLXUoTpMa_9s10NVk4dBQljNOaOXAOhcE0
2019 BAN 421 Masters course Data structures: https://rsbivand.github.io/ban421_h19/