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Wasserstein barycenter regression: application to the joint dynamics of regional GDP and life expectancy in Italy

Author

Listed:
  • Susanna Levantesi

    (Sapienza University of Rome)

  • Andrea Nigri

    (University of Foggia)

  • Paolo Pagnottoni

    (University of Insubria)

  • Alessandro Spelta

    (University of Pavia)

Abstract

We propose to investigate the joint dynamics of regional gross domestic product and life expectancy in Italy through Wasserstein barycenter regression derived from optimal transport theory. Wasserstein barycenter regression has the advantage of being flexible in modeling complex data distributions, given its ability to capture multimodal relationships, while maintaining the possibility of incorporating uncertainty and priors, other than yielding interpretable results. The main findings reveal that regional clusters tend to emerge, highlighting inequalities in Italian regions in economic and life expectancy terms. This suggests that targeted policy actions at a regional level fostering equitable development, especially from an economic viewpoint, might reduce regional inequality. Our results are validated by a robustness check on a human mobility dataset and by an illustrative forecasting exercise, which confirms the model’s ability to estimate and predict joint distributions and produce novel empirical evidence.

Suggested Citation

  • Susanna Levantesi & Andrea Nigri & Paolo Pagnottoni & Alessandro Spelta, 2025. "Wasserstein barycenter regression: application to the joint dynamics of regional GDP and life expectancy in Italy," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(2), pages 313-336, June.
    • Handle: RePEc:spr:alstar:v:109:y:2025:i:2:d:10.1007_s10182-024-00506-1
    DOI: 10.1007/s10182-024-00506-1
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    1. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2021. "Multimodality In Macrofinancial Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 861-886, May.
    2. Giuseppina Bozzo & Susanna Levantesi & Massimiliano Menzietti, 2021. "Longevity risk and economic growth in sub-populations: evidence from Italy," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 101-115, June.
    3. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    4. NIGRI, ANDREA & Aburto, José Manuel & Basellini, Ugofilippo & Bonetti, Marco, 2022. "Evaluation of age-specific causes of death in the context of the Italian longevity transition," SocArXiv t7ms9_v1, Center for Open Science.
    5. Chiang, Wen-Hao & Liu, Xueying & Mohler, George, 2022. "Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates," International Journal of Forecasting, Elsevier, vol. 38(2), pages 505-520.
    6. Chao Zhang & Piotr Kokoszka & Alexander Petersen, 2022. "Wasserstein autoregressive models for density time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 30-52, January.
    7. Maria Elena Giuli & Alessandro Spelta, 2023. "Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices," Computational Management Science, Springer, vol. 20(1), pages 1-17, December.
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