<p>We consider cross-sectional data that exhibit no spatial correlation, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of financial econometrics, entailing a form of spatial autoregression, can explain such behaviour. The parameters are estimated by pseudo Gaussian maximum likelihood based on log-transformed squares, and consistency and asymptotic normality are established. Asymptotically valid tests for spatial independence are developed.</p>
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Peter Robinson
Working Paper details
- DOI
- 10.1920/wp.cem.2008.2908
- Publisher
- IFS
Suggested citation
Robinson, P. (2008). Large-sample inference on spatial dependence. London: IFS. Available at: https://ifs.org.uk/publications/large-sample-inference-spatial-dependence (accessed: 1 July 2024).
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