Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects
Bianling OU1, Zhihe LONG2, Wenqian LI3
1. School of Management Science and Engineering, Central University of Finance and Economics, Beijing 102206, China;
2. School of Business Administration, South China University of Technology, Guangzhou 510641, China;
3. School of Finance, Renmin University of China, Beijing 100872, China
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.
supported by the National Natural Science Foundation of China (71271088), Beijing Municipal Social Science Foundation (15JGB072) and Humanity and Social Science Youth Foundation of Ministry of Education of China (15YJCZH122)
About author: Bianling OU,E-mail:email@example.com;Zhihe LONG,E-mail:firstname.lastname@example.org;Wenqian LI,E-mail:email@example.com
Cite this article:
Bianling OU,Zhihe LONG,Wenqian LI. Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects[J]. Journal of Systems Science and Information, 2019, 7(4): 330-343.
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