Large-sample inference on spatial dependence

Peter M. Robinson

doi:10.1111/j.1368-423X.2008.00264.x

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Large-sample inference on spatial dependence

2009-01-01

Peter M. Robinson *

DOI：10.1111/j.1368-423X.2008.00264.x

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Abstract

En 中文

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.

Keywords:

Asymptotic theory

Independence testing

Parameter estimation

Spatial dependence

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