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Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?

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  • Jing Zeng

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  • Jing Zeng, 2017. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 74-90, January.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:1:p:74-90
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    Cited by:

    1. Francis Kipkogei & Ignace H. Kabano & Belle Fille Murorunkwere & Nzabanita Joseph, 2021. "Business success prediction in Rwanda: a comparison of tree-based models and logistic regression classifiers," SN Business & Economics, Springer, vol. 1(8), pages 1-19, August.
    2. Gilberto Boaretto & Marcelo C. Medeiros, 2023. "Forecasting inflation using disaggregates and machine learning," Papers 2308.11173, arXiv.org.
    3. He Jiang, 2022. "A novel robust structural quadratic forecasting model and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1156-1180, September.
    4. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    5. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.

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