Abstract
Issues relating to spatially autocorrelated disturbance terms are often considered in regional econometric models.1 Although various models have been suggested to describe such spatial correlation, one of the most widely used models is a spatial autoregressive (AR) model which was originally suggested by Whittle (1954) and then extensively studied by Cliff and Ord (1973).2 In the model the regression disturbance vector is viewed as the sum of two parts. One of these parts involves the product of a spatial weighting matrix and a scalar parameter, say p; the other is a random vector whose elements are typically assumed to be independent and identically distributed (i.i.d.) with zero mean and finite variance. We will henceforth refer to this random vector as the innovation vector, so as to distinguish it from the disturbance vector.
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Kelejian, H.H., Robinson, D.P. (1995). Spatial Correlation: A Suggested Alternative to the Autoregressive Model. In: Anselin, L., Florax, R.J.G.M. (eds) New Directions in Spatial Econometrics. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79877-1_3
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DOI: https://doi.org/10.1007/978-3-642-79877-1_3
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