IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i2p753-780.html
   My bibliography  Save this article

Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe

Author

Listed:
  • Pietro Giorgio Lovaglio

Abstract

This paper describes the use of cross‐learning with panel data modeling for stacking regressions of different predictive models for time series employment across occupations in Europe during the last 15 years. The ARIMA and state space models were used for the predictions on the first‐level model ensemble. On the second level, the time series predictions of these models were combined for stacking, using panel data estimators as a cross‐learner and also exploiting the strong hierarchical data structure (time series nested in occupational groups). Very few methods adopt stacking to generate ensembles for time series regressions. Indeed, to the best of our knowledge, panel data modeling has never before been used as a cross‐learner in staking strategies. Empirical application was used to fit employment by occupations in 30 European countries between 2010 Q1 and 2022 Q4, using the last year as the test set. The empirical results show that using panel data modeling as a multivariate time series cross‐learner that stacks univariate time series base models—especially when they do not produce accurate predictions—is an alternative worthy of consideration, also with respect to such classical aggregation schemes as optimal and equal weighting.

Suggested Citation

  • Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.
  • Handle: RePEc:wly:jforec:v:44:y:2025:i:2:p:753-780
    DOI: 10.1002/for.3224
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3224
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.3224?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:44:y:2025:i:2:p:753-780. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.