Predictions in Spatial Econometric Models: Application to Unemployment Data

Thibault Laurent () and Paula Margaretic ()
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Paula Margaretic: TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse
Authors registered in the RePEc Author Service: Abdelaati Daouia ()

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Abstract: In the context of localized unemployment rates in France, we study the issue of spatial econometric model prediction for suratal data, applying the prediction formulas collected and derived in Goulard et al. (Spatial Economic Analysis, 12(2-3), 304-325, 2017), (2017). To model regional unemployment by accounting for 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 prediction can be a complementary method to test procedures for model comparison.

Date: 2021-06
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Published in Advances in Contemporary Statistics and Econometrics, Advances in Contemporary Statistics and Econometrics, pp.409-426, 2021, 978-3-030-73248-6. ⟨10.1007/978-3-030-73249-3_21⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03299374

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

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