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Testing for Spatial Correlations with Randomly Missing Observations in the Dependent Variable

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We consider LM tests for spatial correlations in the spatial error model (SEM) and spatial autoregressive model (SAM) with randomly missing data in the dependent variable. We derive the formulas of the LM test statistics and provide finite sample performance of the LM tests through Monte Carlo experiments.
Cite this paper
Gao, J. and Wang, W. (2014) Testing for Spatial Correlations with Randomly Missing Observations in the Dependent Variable. Theoretical Economics Letters, 4, 623-633. doi: 10.4236/tel.2014.48079.

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http://dx.doi.org/10.1007/978-94-015-7799-1
[2] Burridge, P. (1980) On the Cliff-Ord Test for Spatial Autocorrelation. Journal of the Royal Statistical Society, 42, 107-108.
[3] Wang, W, and Lee, L. (2013a) Estimation of Spatial Autoregressive Models with Randomly Missing Data in the Dependent Variable. Econometrics Journal, 16, 73-102. http://dx.doi.org/10.1111/j.1368-423X.2012.00388.x
[4] LeSage, J. and Pace, R.K. (2004) Models for Spatially Dependent Missing Data. Journal of Real Estate Finance and Economics, 29, 233-254. http://dx.doi.org/10.1023/B:REAL.0000035312.82241.e4
[5] Wang, W, and Lee, L. (2013) Estimation of Spatial Panel Data Models with Randomly Missing Data in the Dependent Variable. Regional Science and Urban Economics, 43, 521-538.
http://dx.doi.org/10.1016/j.regsciurbeco.2013.02.001
[6] Arraiz, I., Drukker, D., Kelejian, H. and Prucha, I. (2008) A Spatial Cliff-Ord-Type Model with Heteroskedastic Innovations: Small and Large Sample Results. CESIFO Working Paper No. 2485.                  eww141009lx
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