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Cox-type tests for competing spatial autoregressive models with spatial autoregressive disturbances

Fei Jin and Lung-Fei Lee ()

Regional Science and Urban Economics, 2013, vol. 43, issue 4, 590-616

Abstract: In this paper, we consider the Cox-type tests of non-nested hypotheses for spatial autoregressive (SAR) models with SAR disturbances. We formally derive the asymptotic distributions of the test statistics. In contrast to regression models, we show that the Cox-type and J-type tests for non-nested hypotheses in the framework of SAR models are not asymptotically equivalent under the null hypothesis. The Cox test in a non-spatial setting has been found often to have large size distortion, which can be removed by bootstrap. Cox-type tests for SAR models with SAR disturbances may also have a large size distortion. We show that the bootstrap is consistent for Cox-type tests in our framework. Performances of the Cox-type and J-type tests as well as their bootstrapped versions in finite samples are compared via a Monte Carlo study. These tests are of particular interest when there are competing models with different spatial weight matrices. Using bootstrapped p-values, the Cox tests have relatively high power in all experiments and can outperform J-type and several other related tests in some cases.

Keywords: Specification; Spatial autoregressive model; Non-nested; Cox test; J test; QMLE (search for similar items in EconPapers)
JEL-codes: C12 C21 C52 R15 (search for similar items in EconPapers)
Date: 2013
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