About predictions in spatial autoregressive models: Optimal and almost optimal strategies

Christine Thomas-Agnan, Thibault Laurent and Michel Goulard

No 13-452, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: We address the problem of prediction in the spatial autoregressive SAR model for areal data which is classically used in spatial econometrics. With the Kriging theory, prediction using Best Linear Unbiased Predictors is at the heart of the geostatistical literature. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the spatial autoregressive SAR models for out-of-sample prediction simultaneously at several sites. We propose a more tractable \almost best" alternative and clarify the relationship between the BLUP and a proper EM-algorithm predictor. From an empirical perspective, we present data-based simulations to compare the efficiency of the classical formulas with the best and almost best predictions.

Keywords: Spatial simultaneous autoregressive models; out of sample prediction; best linear unbiased prediction (search for similar items in EconPapers)
Date: 2013-12, Revised 2016-12
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: About predictions in spatial autoregressive models: optimal and almost optimal strategies (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:27788

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