About predictions in spatial autoregressive models: Optimal and almost optimal strategies
AbstractWe address the problem of prediction in the classical spatial autoregressive lag model for areal data. In contrast with the spatial econometrics literature, the geostatistical literature has devoted much attention to prediction using the Best Linear Unbiased Prediction approach. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the spatial autoregressive lag models for in sample prediction as well as out-of-sample prediction simultaneously at several sites. We propose a more tractable “almost best” alternative. From an empirical perspective, we present data-based simulations to compare the efficiency of the classical formulas with the best and almost best predictions.
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Bibliographic InfoPaper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 13-452.
Date of creation: 18 Dec 2013
Date of revision:
Spatial simultaneous autoregressive models; out of sample prediction; best linear unbiased prediction;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-01-17 (All new papers)
- NEP-ECM-2014-01-17 (Econometrics)
- NEP-GEO-2014-01-17 (Economic Geography)
- NEP-URE-2014-01-17 (Urban & Real Estate Economics)
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- Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
- James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, 09.
- Doucet, Romain & Margaretic, Paula & Thomas-Agnan, Christine & Villotta, Quentin, 2014. "Spatial dependence in (origin-destination) air passenger flows," TSE Working Papers 14-494, Toulouse School of Economics (TSE).
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