Random Effects And Spatial Autocorrelation With Equal Weights
This note considers a panel data regression model with spatial autoregressive disturbances and random effects where the weight matrix is normalized and has equal elements. This is motivated by Kelejian et al. (2005), who argue that such a weighting matrix, having blocks of equal elements, might be considered when units are equally distant within certain neighborhoods but unrelated between neighborhoods. We derive a simple weighted least squares transformation that obtains GLS on this model as a simple OLS. For the special case of a spatial panel model with no random effects, we obtain two sufficient conditions where GLS on this model is equivalent to OLS. Finally, we show that these results, for the equal weight matrix, hold whether we use the spatial autoregressive specification, the spatial moving average specification, the spatial error components specification or the Kapoor et al. (2005) alternative to modeling panel data with spatially correlated error components.
(This abstract was borrowed from another version of this item.)
Volume (Year): 22 (2006)
Issue (Month): 05 (October)
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References listed on IDEAS
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- Badi H. Baltagi & Seuck Heun Song & Won Koh, 2002.
"Testing Panel Data Regression Models with Spatial Error Correlation,"
10th International Conference on Panel Data, Berlin, July 5-6, 2002
B6-4, International Conferences on Panel Data.
- Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
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- 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.
- Magnus, Jan R., 1982. "Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 239-285, August.
- Magnus, J.R., 1982. "Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood," Other publications TiSEM 9ffb33fe-f5af-470f-b405-f, Tilburg University, School of Economics and Management.
- Holtz-Eakin, Douglas, 1994. "Public-Sector Capital and the Productivity Puzzle," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 12-21, February.
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