Advances in Spatial Econometrics: Methodology, Tools and by Luc Anselin, Raymond Florax, Sergio J. Rey

By Luc Anselin, Raymond Florax, Sergio J. Rey

Advances in Spatial Science

This sequence of books is devoted to reporting on fresh advances in spatial technological know-how. It comprises clinical reviews concentrating on spatial phenomena, making use of theoretical frameworks, analytical tools, and empirical tactics in particular designed for spatial research. The sequence brings jointly leading edge spatial study making use of options, views, and techniques with a relevance to either easy technological know-how and coverage making. the purpose is to provide advances in spatial technological know-how to an educated readership in universities, examine firms, and policy-making associations during the world.

The form of fabric thought of for booklet within the sequence includes:

- Monographs of theoretical and utilized learn in spatial science;

- state of the art volumes in components of uncomplicated research;

- reviews of leading edge theories and strategies in spatial science;

- Tightly edited stories shape in particular organised study seminars.

Manuscripts has to be ready based on the tips for authors and editors which may be acquired from Springer-Verlag. Manuscripts thought of for the sequence could be reviewed through self reliant specialists to make sure their originality, clinical point, and overseas coverage relevance.

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Sample text

For example, an extension to the Rats time series package (available from the Rats support pages) implements the Driscoll and Kraay (1998) spatial correlation consistent covariance matrix estimator for panel data. 9 As an increasingly attractive alternative to the use of toolboxes that operate as extensions to commercial software, there is a very active community involved in developing statistical sofware in the open source R environment. 10 This has led to an extensive collection of functions to analyze spatial data, including descriptive spatial autocorrelation statistics and the full range of spatial regression analyses in Roger Bivand's spdep package (see Bivand and Gebhardt, 2000; Bivand, 2002b, as well as the Bivand-Portnov chapter in Part I of this volume).

Spatial heterogeneity can be satisfactorily dealt with utilizing concurrent standard techniques from mainstream econometrics. SpatiallY induced heteroskedasticity can be handled using a generalized least squares (GLS) estimator, or White-adjusted variances. Substantive spatial heterogeneity can be incorporated through specifications allowing for spatial regimes. For spatial dependence, however, there are neither standard econometric tests nor standard estimators that adequately account for the specific nature of spatial dependence (An selin and Bera, 1998; Anselin, 2001b).

6 explains the specification of the metaregression, and presents the results of the meta-analysis. Finally, Sect. 7 contains conclusions, and delivers useful practical guidelines for the selection and interpretation of test statistics for spatial dependence in specific research contexts. 2 Meta-Analysis and Response Surfaces In our analysis, we use the conventional statistical technique of multivariate regression analysis to synthesize the results of previous studies dealing with Monte Carlo simulation of spatial dependence testing in spatial econometrics.

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