Large-sample inference on spatial dependence

Peter Robinson ()
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Peter Robinson: Institute for Fiscal Studies and London School of Economics

No CWP29/08, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract:

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.

New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Date: 2008-10
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Persistent link: http://EconPapers.repec.org/RePEc:ifs:cemmap:29/08

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