Random effects and Spatial Autocorrelations with Equal Weights

Badi Baltagi

No 89, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: 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.

Keywords: Panel data; spatial error correlation; equal weights; error components (search for similar items in EconPapers)
JEL-codes: C12 C23 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2006-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Journal Article: RANDOM EFFECTS AND SPATIAL AUTOCORRELATION WITH EQUAL WEIGHTS (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:89

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