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

Michel Goulard, Thibault Laurent and Christine Thomas-Agnan

Spatial Economic Analysis, 2017, vol. 12, issue 2-3, 304-325

Abstract: About predictions in spatial autoregressive models: optimal and almost optimal strategies. Spatial Economic Analysis. This paper addresses the problem of prediction in the spatial autoregressive (SAR) model for areal data, which is classically used in spatial econometrics. With kriging theory, prediction using the best linear unbiased predictors (BLUPs) is at the heart of the geostatistical literature. From a methodological point of view, we explore the limits of the extension of BLUP formulas in the context of 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 expectation–maximization (EM) algorithm predictor. From an empirical perspective, we present data-based simulations to compare the efficiency of classical formulas with the best and almost best predictions.

Date: 2017
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:specan:v:12:y:2017:i:2-3:p:304-325

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DOI: 10.1080/17421772.2017.1300679

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