Predictions in spatial econometric models: Application to unemployment data

Thibault Laurent; Paula Margaretic

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

Predictions in spatial econometric models: Application to unemployment data

Thibault Laurent, Paula Margaretic

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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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.

Original language	English
Title of host publication	Advances in Contemporary Statistics and Econometrics
Subtitle of host publication	Festschrift in Honor of Christine Thomas-Agnan
Publisher	Springer International Publishing
Pages	409-426
Number of pages	18
ISBN (Electronic)	9783030732493
ISBN (Print)	9783030732486
DOIs	https://doi.org/10.1007/978-3-030-73249-3_21
State	Published - 14 Jun 2021
Externally published	Yes

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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.",

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AB - 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.

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Predictions in spatial econometric models: Application to unemployment data

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