Predictions in Spatial Econometric Models: Application to Unemployment Data

Thibault Laurent () and Paula Margaretic ()
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Thibault Laurent: University of Toulouse, Toulouse School of Economics, CNRS
Paula Margaretic: University of San Andrés

A chapter in Advances in Contemporary Statistics and Econometrics, 2021, pp 409-426 from Springer

Abstract: Abstract In the context of localized unemployment rates in France, we study the issue of prediction of spatial econometric models for areal data, by applying the prediction formulas gathered and derived in Goulard et al. (Spatial Economic Analysis, 12(2–3), 304–325, 2017), (2017). To model regional unemployment taking into account local interactions, we estimate several spatial econometric model specifications, namely, the spatial autoregressive SAR and SDM models, as well as the SLX model. We consider both types of predictions, namely, in-sample and out-of-sample prediction. We show that the prediction can be a complementary method to testing procedures for model comparison.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73249-3_21

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DOI: 10.1007/978-3-030-73249-3_21

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