Spatial Econometrics
- Credits: 5
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 2
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Upon successful completion of the course, students will acquire the following knowledge:
- basic knowledge of a set of statistical and econometric techniques that allow to deal with the specifics caused by spatial aspects in regional data analysis.
Upon successful completion of the course, students will acquire the following skills:
- ability to use basic techniques of spatial data analysis and spatial econometrics,
- ability to use specialized econometric software, GeoDa and R software.
Upon successful completion of the course, students will acquire the following competencies:
- practical skills and competencies associated with the application of models and methods of spatial data analysis and spatial econometrics in the analysis of specific tasks using adequate software (GeoDa, R).
Indicative content
1. Spatial data. Visualization of spatial data - graphs, maps.
2. Spatial effects. Spatial autocorrelation and heterogeneity. Construction of spatial weight matrix - contiguity weights, distance weights.
3. Spatial autocorrelation testing. Moran’s I statistics, Geary’s C statistics, Getis-Ord G statistics.
4. Global and local spatial statistics, LISA.
5. Bivariate and multivariate local spatial statistics.
6. Spatial econometric models. Spatial autocorrelation diagnostics in regression model. Classification of spatial econometric models.
7. Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM). Spatial method of maximum likelihood.
8. Spatial Autoregressive Model (SAR) and Spatial Durbin Model (SDM). Spatial two-stage least squares method.
9. SARAR and SLX model. Interpretation of parameters in spatial econometric models.
10. Direct, indirect and total effects in spatial econometric models. Spatial decomposition of effects.
11. Spatial heterogeneity. Basic specifications of spatial regimes.
12. Spatial heterogeneity. Geographically weighted regression (GWR).
13. Kernel weights in GWR method. Mixed GWR method.
Support literature
1. ANSELIN, L. – REY, S. J. 2014. Modern Spatial Econometrics in Practice. Chicago: GeoDa Press LLC, 2014. 354 p. ISBN 0986342106
2. ARBIA, G. 2014. A Primer for Spatial Econometrics. Berlin Heidelberg: Springer-Verlag, 2006. 207 p. ISBN-10 3-540-32304-X.
3. FOTHERINGHAM, A. S., BRUNSDON, C., CHARLTON, M. E. 2002. Geographically Weighted Regression. The Analysis of Spatial Varying Relationships. Chichester: Wiley.
Requirements to complete the course
30 % work at seminars and writing of projects
70 % combined final exam
Student workload
Total study load (in hours): 5 credits x 26 hours = 130 hours
26 hours lecture attendance
26 hours seminar attendance
26 hours preparation for seminars
26 hours writing a seminar paper
26 hours preparation for final exam
Date of approval: 10.02.2023
Date of the latest change: 16.05.2022