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Large-sample inference on spatial dependence

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  • P. M. Robinson

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. Copyright (C) The Author(s). Journal compilation (C) Royal Economic Society 2009

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  • P. M. Robinson, 2009. "Large-sample inference on spatial dependence," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 68-82, January.
  • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:s1:p:s68-s82
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2008.00264.x
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    Cited by:

    1. Jenish, Nazgul, 2012. "Nonparametric spatial regression under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 167(1), pages 224-239.
    2. Takaki Sato & Yasumasa Matsuda, 2016. "Spatial Autoregressive Conditional Heteroscedasticity Model and Its Application," TERG Discussion Papers 348, Graduate School of Economics and Management, Tohoku University.
    3. Joris Pinkse & Margaret E. Slade, 2010. "The Future Of Spatial Econometrics," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 103-117.
    4. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.

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