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Predictions in Spatial Econometric Models: Application to Unemployment Data

In: Advances in Contemporary Statistics and Econometrics

Author

Listed:
  • Thibault Laurent

    (University of Toulouse, Toulouse School of Economics, CNRS)

  • Paula Margaretic

    (University of San Andrés)

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.

Suggested Citation

  • Thibault Laurent & Paula Margaretic, 2021. "Predictions in Spatial Econometric Models: Application to Unemployment Data," Springer Books, in: Abdelaati Daouia & Anne Ruiz-Gazen (ed.), Advances in Contemporary Statistics and Econometrics, pages 409-426, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-73249-3_21
    DOI: 10.1007/978-3-030-73249-3_21
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