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Simultaneous Spatial and Functional Form Transformations

In: Advances in Spatial Econometrics

Author

Listed:
  • R. Kelley Pace

    (Louisiana State University)

  • Ronald Barry

    (University of Alaska)

  • V. Carlos Slawson

    (Louisiana State University)

  • C. F. Sirmans

    (University of Connecticut)

Abstract

Technological advances such as the global positioning system (GPS) and low-cost, high-quality geographic information systems (GIS) have led to an explosion in the volume of large data sets with locational coordinates for each observation. For example, the Census provides large amounts of data for over 250,000 locations in the US (block groups). Moreover, geographic information systems can often provide approximate locational coordinates for street addresses (geocoding). Given the volume of business information, which contains a street address field, this allows the creation of extremely large spatial data sets. Such data, as well as other types of spatial data, often exhibit spatial dependence and thus require spatial statistical methods for efficient estimation, valid inference, and optimal prediction.

Suggested Citation

  • R. Kelley Pace & Ronald Barry & V. Carlos Slawson & C. F. Sirmans, 2004. "Simultaneous Spatial and Functional Form Transformations," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 9, pages 197-224, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-05617-2_9
    DOI: 10.1007/978-3-662-05617-2_9
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    Citations

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    Cited by:

    1. Li Dong & Le Canh, 2010. "Nonlinearity and Spatial Lag Dependence: Tests Based on Double-Length Regressions," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-18, June.
    2. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.

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