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

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.