An introduction to Spatial Econometrics: Methods and Applications

5.0 hp

2025-10-13 - 2025-10-31

Uppsala

English

Application Code: SLU-F0053

Course Code: P000149

Application deadline: 2025-10-10

Course facts

Subject: Other social science
Credits: 5.0 credits
Course code: P000149
Application deadline: 2025-10-10
Study location: Uppsala
Form of instruction: Campus-based instruction
Course language: English
Responsible Department/Equivalent: Department of Economics
Rate of study: 100 %

P000149, An introduction to Spatial Econometrics: Methods and Applications, 5.0 Hp
Print syllabus

Syllabus

Finalized by: Jens Rommel, 2025-09-04
Valid from :

Level

Third cycle

Subject

Other social science

Grading Scale

Pass / Fail

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

Admittance to a PhD program.

Objectives

At the end of the course, students are expected to be able to:
1. Quantify spatial dependence in empirical variables
2. Appreciate and interpret spatial aspects of economic relationships from geographic and nongeographic perspectives
3. Understand spatial econometric estimation methods
4. Explain the conditions for identification of spatial models
5. Use statistical/econometrics software to estimate models with spatial elements
6. Test and interpret estimated relationships that contain spatial elements

Content

This course provides an introduction to spatial econometrics. Spatial econometrics is concerned with the spatial aspects present in cross-sectional and space-time observations. "Space" is interpreted not merely in a geographic sense, but also in economic or sociological terms. The course is specifically designed for PhD students with a basic background in econometrics.

Examination Formats and Requirements for Passing the Course

Students will deliver a seminar presentation on their own research or replicate a provided study demonstrating the purpose and importance of spatial econometric techniques. The presentation should clearly explain the methodological steps taken, show how potential sources of error were identified and addressed, and provide a well-structured interpretation of the results.

Responsible Department/Equivalent

Department of Economics

Supplementary information

Other Information

The primary software applications used in this course will be R, which is “free and open source software”. Stata may be used sparingly in the course.

Recommended books and supplementary materials:

Kelejian, H., & Piras, G. (2017). Spatial econometrics. Academic Press.

Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels (Vol. 479, p. 480). Heidelberg: Springer.

Kopczewska, K. (2020). Applied spatial statistics and econometrics: data analysis in R. Routledge.

L. Anselin (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer.

Bivand, Roger S., Pebesma, Edzer J., Gómez-Rubio, Virgilio (2008*), Applied Spatial Data Analysis with R, Springer.*

Angrist, J.D., and A.B. Krueger. 1991. “Does compulsory school attendance affect schooling and earnings?” *The Quarterly journal of economics *106(4): 979–1014.

Anselin, L. and S. Ray. 2014. “Two stage least squares.” In: Modern Spatial Econometrics in Practice: A guide to GeoDa, GeoDaSpace, and PySAL, pp. 139-142. GeoDa Press LLC*. *

Baum-Snow, N., M.E. Kahn & R. Voith. 2005. “Effects of urban rail transit expansions: Evidence from sixteen cities, 1970-2000.” Brookings-Wharton Papers on Urban Affairs, 147-206.

LeSage, J.P., and Christine Thomas-Agnan. 2015. “interpreting spatial econometric origin-destination flow models.” *Journal of regional science *55, no. 2: 188–208.

Lukongo, O. E., & T. Miller. 2018. “Evaluating the spatial consequence of interest rate ceiling using a spatial regime change approach.” The American Economist, 63(2), 166-186.

Proost, S., & J. F. Thisse. 2019. “What can be learned from spatial economics?” Journal of Economic Literature, 57(3), 575-643.

Schneiter, P., & Mellon-Bedi, S. (2025). The Environmental Kuznets Curve Revisited: A Spatial Panel Model with Heterogeneous Coefficients. Energy Economics, 143, 108237.

Skevas, T., Skevas, I., & Swinton, S. M. (2018). Does spatial dependence affect the intention to make land available for bioenergy crops?. Journal of Agricultural Economics, 69(2), 393-412.

Skevas, I. (2020). Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms. European Journal of Operational Research, 283(1), 356-364.

academic year 2025/2026

An introduction to Spatial Econometrics: Methods and Applications (P000149-F0053)
2025-10-24 - 2025-11-21

Contact

Course coordinator: Shaibu Mellon Bedi