Abstract
Autocorrelation latent in spatial and in temporal random variables interacts in space-time random variables. This interaction can be of two slightly different forms: one describes a contemporaneous, whereas the other describes a lagged, specification of space-time structure. The spatial Moran Coefficient can be extended to index space–time data dependencies for either of these specifications. This chapter presents some of its statistical distribution theory, using this extension as a foundation for eigenvector space–time filter construction. This class of constructed filters can address a number of spatial statistical/econometric problems, such as analysis impacts of omitted variables.
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These binomial RVs have been standardized to a denominator of 100 to control for variation due to local labor market size. Furthermore, these RVs need to be divided by the national percentage in their corresponding employment category to convert them to LQs.
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Griffith, D.A., Paelinck, J.H.P. (2018). Space–Time Autocorrelation. In: Morphisms for Quantitative Spatial Analysis. Advanced Studies in Theoretical and Applied Econometrics, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-72553-6_3
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DOI: https://doi.org/10.1007/978-3-319-72553-6_3
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