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Type: Journal Articles Authors: Peter Robinson
Published in: Econometrics Journal
Volume, issue, pages: Vol. 12 (s1), pp. s68-s82
Previous version: cemmap Working Papers [Details]
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.
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