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Large-sample inference on spatial dependence

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  • Peter Robinson

    (Institute for Fiscal Studies and London School of Economics)

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.

Suggested Citation

  • Peter Robinson, 2008. "Large-sample inference on spatial dependence," CeMMAP working papers CWP29/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:29/08
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    File URL: http://cemmap.ifs.org.uk/wps/cwp2908.pdf
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    References listed on IDEAS

    as
    1. Craig Brett & Joris Pinkse, 1997. "Those Taxes are all over the Map! A Test for Spatial Independence of Municipal Tax Rates in British Columbia," International Regional Science Review, , vol. 20(1-2), pages 131-151, April.
    2. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    3. Robinson, P.M., 2008. "Correlation testing in time series, spatial and cross-sectional data," Journal of Econometrics, Elsevier, vol. 147(1), pages 5-16, November.
    4. Peter Robinson, 2007. "Correlation testing in time series, spatial and cross-sectional data," CeMMAP working papers CWP01/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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