Spatial lag test with equal weights
This note shows that for a spatial regression with equal weights, the LM test is always equal to NÂ /Â 2(NÂ -Â 1), where N is the sample size. This means that this test statistics is a function of N and not a function of the spatial parameter [rho]. In fact, this test statistic tends to one half for N tending to infinity. The null hypothesis of no spatial correlation is never rejected no matter what [rho] is.
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References listed on IDEAS
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- Harry H. Kelejian & Ingmar R. Prucha & Yevgeny Yuzefovich, 2006. "Estimation Problems In Models With Spatial Weighting Matrices Which Have Blocks Of Equal Elements," Journal of Regional Science, Wiley Blackwell, vol. 46(3), pages 507-515.
- Kelejian, Harry H. & Prucha, Ingmar R., 2002. "2SLS and OLS in a spatial autoregressive model with equal spatial weights," Regional Science and Urban Economics, Elsevier, vol. 32(6), pages 691-707, November.
- Lee, Lung-Fei, 2002. "Consistency And Efficiency Of Least Squares Estimation For Mixed Regressive, Spatial Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 18(02), pages 252-277, April.
- Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
- Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
- Badi H. Baltagi, 2006.
"Random effects and Spatial Autocorrelations with Equal Weights,"
Center for Policy Research Working Papers
89, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi, Badi H., 2006. "Random Effects And Spatial Autocorrelation With Equal Weights," Econometric Theory, Cambridge University Press, vol. 22(05), pages 973-984, October.
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