By Luc Anselin, Raymond Florax, Sergio J. Rey
Advances in Spatial Science
This sequence of books is devoted to reporting on contemporary advances in spatial technology. It comprises medical stories concentrating on spatial phenomena, making use of theoretical frameworks, analytical equipment, and empirical systems in particular designed for spatial research. The sequence brings jointly cutting edge spatial examine using techniques, views, and strategies with a relevance to either uncomplicated technological know-how and coverage making. the purpose is to give advances in spatial technological know-how to an educated readership in universities, learn companies, and policy-making associations through the world.
The form of fabric thought of for booklet within the sequence includes:
- Monographs of theoretical and utilized study in spatial science;
- cutting-edge volumes in components of uncomplicated research;
- stories of cutting edge theories and techniques in spatial science;
- Tightly edited experiences shape particularly organised examine seminars.
Manuscripts needs to be ready in keeping with the tips for authors and editors which may be bought from Springer-Verlag. Manuscripts thought of for the sequence may be reviewed via self reliant specialists to make sure their originality, clinical point, and foreign coverage relevance.
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Additional info for Advances in Spatial Econometrics: Methodology, Tools and Applications
Recent additions to the rnisspecification toolbox include tests for simultaneous equation models (Anselin and Kelejian, 1997), the combination of heteroskedasticity and spatial autocorrelation (Kelejian and Robinson, 1998), and spatial error component models (Anselin and Moreno, 2003; see also Kelejian and Yuzefovic, 2001). 2 Given the analytical intractability of the small sample distribution of the test statistics, extensive simulation experiments are performed to assess the size and the power of tests for spatial dependence in finite samples.
Positive spatially correlated heteroskedasticity leads to a higher probability of rejecting the null, while the reverse holds when the heteroskedasticity is negatively correlated. In both instances the large sample properties of the classic tests no longer hold. However, Kelejian and Robinson also show that when the heteroskedasticity is not spatially correlated, there is no effect on the asymptotic properties of the tests for spatial correlation. This important contribution provides a basis for extending current model specification strategies to consider spatial heteroskedasticity as well as spatial correlation.
Using data on 157 urban localities, Bivand and Portnov compare the connectedness characteristics of different spatial weights and provide illustrative R code to demonstrate the practical implementation of these concepts. They use the weights in an analysis of spatial autocorrelation in the percentage popUlation change during the second half of the 1990s. The results illustrate how one can explore the spatial dependence in "realistic but challenging" distributions using the R programming environment.
Advances in Spatial Econometrics: Methodology, Tools and Applications by Luc Anselin, Raymond Florax, Sergio J. Rey