Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

Ou Bianling (), Long Zhihe () and Li Wenqian ()
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Ou Bianling: School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 102206, China
Long Zhihe: School of Business Administration, South China University of Technology, Guangzhou, 510641, China
Li Wenqian: School of Finance, Renmin University of China, Beijing, 100872, China

Journal of Systems Science and Information, 2019, vol. 7, issue 4, 330-343

Abstract: This paper applies bootstrap methods to LM tests (including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu (2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((NT)−2) and that of fast double bootstrap LM tests is O((NT)−5/2). Extensive Monte Carlo experiments suggest that, compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level. Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

Keywords: fast double bootstrap; LM-lag test; LM-error test; panel data models with fixed effects; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:7:y:2019:i:4:p:330-343:n:3

DOI: 10.21078/JSSI-2019-330-14

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