Spatial Econometrics in R 2400-ZEWW404

Upon completion of the course, the student:

KNOWLEDGE

Has in-depth knowledge and understanding of methods and tools for describing economic and social phenomena from a spatial perspective; spatial statistics and spatial models; sources of regional data acquisition; and methods of using advanced statistical software to describe economic and social phenomena.

Through working with Open Source licensed software and using teaching materials developed at the Faculty of Economic Sciences, University of Warsaw (WNE UW), understands the basic concepts and principles of industrial property protection and copyright law, and is able to use tools made available under Open Source and Creative Commons licenses.

Knows and understands the practical applications of the presented statistical methods and, based on them, is able to prepare analyses for market research purposes in professional work or for their own business activity.

Understands, based on spatial and panel data analysis, socio-economic spatial structures and their changes over time; the impact of space on socio-economic processes; regional diversity and similarity; and spatial regimes.

SKILLS

Is able, through critical analysis of statistical research results and economic theory, to apply theoretical knowledge to describe and analyze the causes and course of social processes and phenomena; can formulate independent opinions and critically select data and analytical methods.

Is able to acquire regional data and vector maps and, using advanced R-CRAN software, graphically present spatial data, calculate basic spatial statistics, estimate a spatial econometric model, and draw conclusions about spatial relationships based on the results obtained.

Is able to conduct a complete spatial analysis: search for data, apply statistical or econometric description and modeling, and present the entire research process in written form and orally as a report.

SOCIAL COMPETENCES

Demonstrates familiarity with advanced statistical software, enabling further self-directed learning and providing a solid introduction to object-oriented programming.

Is prepared to critically assess the presented models and to properly identify and resolve dilemmas related to the application of these methods in professional work or in running their own business.

To pass the course, the following is required:

1. Individual research project (50%)

An individual quantitative research project of a spatial nature, prepared independently in a two-person group. Both theoretical papers (e.g., comparison of methods, evaluation of methodological properties) and thematic papers (empirical data analysis) are allowed.

The research project must include:

Introduction to the topic and formulation of a research hypothesis

Description of the data – source, spatial variation, and possible changes over time

Specification of the econometric problem/model and expected results

Model estimation and diagnostics / quantitative spatial analysis

Interpretation of results and conclusions

2. Review of an assigned article (50%)

A written (critical) review of a text selected by the instructor (texts in English).

The article review must include:

The aim and scope of the study – research questions/hypotheses, data used, geographical area

Spatial methods applied in the study, together with the student’s own assessment of their appropriateness – discussion of the purpose of using a given method and the expectations regarding the results

Research results (general overview) – whether the research question was successfully addressed and whether spatial methods provided additional insights compared to classical methods

Overall evaluation of the text, comments, remarks, and additional insights (know-how)

Grading scale

The final grade is the average of the project grade and the article review grade.

[0%–50%) – fail (ndst)

[50%–60%) – satisfactory (dst)

[60%–70%) – satisfactory plus (dst+)

[70%–80%) – good (db)

[80%–90%) – good plus (db+)

[90%–100%] – very good (bdb)

Obligatory reading:
- Kopczewska K., Ekonometria i statystyka przestrzenna, CeDeWu, Warszawa, 2006
- Kopczewska K., Kopczewski T., Wójcik P., (red), 2009, Metody ilościowe w R. Aplikacje ekonomiczne i finansowe, CeDeWu, Warszawa
Supplementary reading:
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC.
- Bivand, R. S., Pebesma, E. J., Gomez-Rubio, V., & Pebesma, E. J. (2008). Applied spatial data analysis with R (Vol. 747248717). New York: Springer.

Chun, Y., & Griffith, D. A. (2013). Spatial statistics and geostatistics: theory and applications for geographic information science and technology. Sage.

Fotheringham, A. S., & Rogerson, P. A. (Eds.). (2008). The SAGE handbook of spatial analysis. Sage.

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

• Description of 2400-ZEWW404 in USOSweb