Adaptive inference on pure spatial models

Jungyoon Lee and Peter M. Robinson

Journal of Econometrics, 2020, vol. 216, issue 2, 375-393

Abstract: In a general class of semiparametric pure spatial models (having no explanatory variables) allowing nonlinearity in the parameter and the weight matrix, we propose adaptive tests and estimates which are asymptotically efficient in the presence of unknown, nonparametric distributional form. Feasibility of adaptive estimation is verified and its efficiency improvement over Gaussian pseudo maximum likelihood is shown to be either less than, or more than, for models with explanatory variables, depending on properties of the spatial weight matrix. An adaptive Lagrange Multiplier testing procedure for lack of spatial dependence is proposed and this, and our adaptive parameter estimate, are extended to cover regression with spatially correlated errors.

Keywords: Efficient test; Adaptive estimation; Spatial models (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C21 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:216:y:2020:i:2:p:375-393

DOI: 10.1016/j.jeconom.2019.10.006

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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