Consistent specification testing under spatial dependence

Abhimanyu Gupta; Xi Qu

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arXiv · 2101.10255

Consistent specification testing under spatial dependence

Abhimanyu Gupta·Xi Qu

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Abstract

We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the `space' being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is asymptotically standard normal. To prove the latter property, we establish a central limit theorem for quadratic forms in linear processes in an increasing dimension setting. Finite sample performance is investigated in a simulation study, with a bootstrap method also justified and illustrated, and empirical examples illustrate the test with real-world data.

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BibTeX RIS

Abhimanyu Gupta, Xi Qu. 2021-01-25. Consistent specification testing under spatial dependence. https://arxiv.org/abs/2101.10255

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