Spatial Econometrics in R 2400-ZEWW404
Methods of spatial econometrics and statistics are used in regional research, real estate market research, natural resources, environmental economics, public sector economics and international economics, innovation, insurance, etc., as well as business locations. Analysis of these issues using classical statistics and econometrics, which ignore spatial dependencies, give incorrect results. Spatial econometrics allow to see the relationship between neighborly observations and include this information in modelling. It complements traditional methods in relation to spatial problems, but requires specific data sets (geo-localized data and contour maps) and specialized econometric-statistical packages.
During the classes, the students get to know the methods for spatial econometrics, from the basics to the level enabling their own research. We use R software (Open Source available from www.r-project.org), so it can be used without restrictions and without costs both in scientific work and for commercial purposes.
Topics discussed:
• What is spatial econometrics? The specificity of spatial research, spatial effects - data, types of spatial dependency, spatial diversity, relationships in space
• Visualization of regional and point data - determination of centroids, layered mapping, operation on spatial geometries
• Spatial weights matrix - construction, properties, operations, usage
• Formalization of spatial dependence - spatial lag operator, spatial autocorrelation measures (Moran’s I, LISA), spatial dependence testing
• Specification and testing of spatial models - models with one (SLX, SLM, SEM), two (SAC, SDM, SDEM) or three (GNS) spatial components, testing: AIC, BIC, LR, and Moran tests for residuals
• Complex models - spatial interaction models, panel models, cumulative models
• Clustering of spatial data, tessellation for point data
• Practical applications of spatial analyzes - based on selected articles
Type of course
Prerequisites (description)
Course coordinators
Learning outcomes
KNOWLEDGE
• Knows in details the methods and tools for describing economic and social phenomena in spatial terms. Knows statistics and spatial models. knows the sources of regional data acquisition. Knows how to use an advanced statistical program in the description of economic and social phenomena.
• By working with an open source licensed program and by using teaching materials created at WNE UW, knows and understands the basic concepts and principles in the field of industrial property and copyright protection and is able to use the tools made available on the principles of Open Source and Creative Commons
• Knows the application possibilities of the statistical methods presented and on this basis can create the business analyses
• On the basis of the spatial and panel data analysis, student has expanded knowledge about social spatial structures and their changes over time. Student is able to assess the influence of space on economic and social processes, analyze analytically the diversity and similarity of regions, define spatial regimes
SKILLS
• By critically analyzing the results of statistical surveys and economic theory student can use theoretical knowledge to describe and analyze the causes and course of social processes and phenomena, and can formulate his own opinions and critically select data and methods of analysis
• Can acquire regional data, map in a vector form and thanks to the ability to work with R software can graphically present spatial data, calculate spatial statistics, estimate the econometric spatial model and draw conclusions about spatial dependencies on the basis of the presented results.
• Is able to carry out spatial analysis. Is able to search for data, apply the description of statistical or econometric modeling. Student can present in writing and pass the entire research process as a report.
SOCIAL COMPETENCE
• Becoming acquainted with an advanced statistical program allows for expanding knowledge on his own and is a good introduction to learn object oriented programming.
• The method of passing the subject allow to be critical in relation to the presented models and correctly identify and resolve dilemmas using these methods in running own business or professional work
KW01, KW02, KW03, KU01, KU02, KU03, KK01, KK02, KK03
Assessment criteria
Own research project (50%) - to carry out a quantitative spatial analysis study alone in a group of two. Possible theoretical works (eg comparison of methods, evaluation of the properties of the method) and thematic (analysis on empirical data).
The research project must include:
- introduction to the subject, putting a research hypothesis
- description of the data - source, spatial diversity, or changes in time
- specification of the problem / econometric model and expectations
- model estimation and diagnostics / spatial quantitative analysis
- interpretation of results and conclusions
Own research project (50%) + review of the assigned article (50%)
Review of the assigned article (50%) - written (critical) review of the test selected by the teacher (texts in English)
The review of the article must include:
- purpose and area of the research - research questions / hypotheses, data used, geographical area
- spatial methods used in the study along with their own opinion on the appropriateness of their use - should be discussed for what purpose a specific method was used, what were the expectations of the results
- results of the study (in general) - was it possible to answer the question asked by the researcher whether spatial methods brought additional information in comparison with classical methods
- general opinion about the text, comments, comments, additional know-how
Bibliography
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
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: