Non-nested testing of spatial correlation

Miguel Delgado () and Peter Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other structure. In the former case, observations may be regularly-spaced across one or more dimensions, as is typical with much spatio-temporal data, or irregularly-spaced across all dimensions; both isotropic models and non-isotropic models can be considered, and a wide variety of correlation structures. In the second case, models involving spatial weight matrices are covered, such as “spatial autoregressive models”. The setting is sufficiently general to potentially cover other parametric structures such as certain factor models, and vector-valued observations, and here our preliminary asymptotic theory for parameter estimates is of some independent value. The test statistic is based on a Gaussian pseudo-likelihood ratio, and is shown to have an asymptotic standard normal distribution under the null hypothesis that one of the two models is correct; this limit theory rests strongly on a central limit theorem for the Gaussian pseudo-maximum likelihood parameter estimates. A small Monte Carlo study of finite-sample performance is included.

Keywords: Non-nested test; spatial correlation; pseudo maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C12 C21 (search for similar items in EconPapers)
Date: 2015-03
New Economics Papers: this item is included in nep-geo
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Citations: View citations in EconPapers (11)

Published in Journal of Econometrics, March, 2015, 187(1), pp. 385-401. ISSN: 0304-4076

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http://eprints.lse.ac.uk/61433/ Open access version. (application/pdf)

Related works:
Journal Article: Non-nested testing of spatial correlation (2015)
Working Paper: Non-Nested Testing of Spatial Correlation (2013)
Working Paper: Non-nested testing of spatial correlation (2013)
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