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Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables

Author

Listed:
  • Philippe Goulet Coulombe
  • Massimiliano Marcellino
  • Dalibor Stevanovic

Abstract

We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle cross-sectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross-sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings. Nous étudions le nowcasting des variables budgétaires des États américains à l’aide de modèles d’apprentissage automatique (machine learning) et de prédicteurs à fréquence mixte, dans un cadre en panel. Les réseaux de neurones intégrant des variables continues et des identifiants catégoriels surpassent systématiquement les alternatives linéaires, en particulier lorsqu’ils sont combinés à des structures en panel mutualisé. Ces architectures permettent de capter les différences entre les États tout en tirant parti des régularités partagées. Les gains de prévision sont particulièrement importants pour les variables volatiles comme les dépenses et les déficits. Le regroupement des données améliore la stabilité des prévisions, et les modèles d’apprentissage automatique sont mieux adaptés pour traiter les non-linéarités transversales. Les résultats montrent que les améliorations prédictives sont généralisées et que même quelques indicateurs infranuels spécifiques aux États contribuent de manière significative à la précision des prévisions. Nos résultats soulignent la complémentarité entre la modélisation flexible et le regroupement transversal, faisant des réseaux de neurones en panel un outil puissant pour un suivi budgétaire rapide et précis dans des contextes hétérogènes.

Suggested Citation

  • Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," CIRANO Working Papers 2025s-15, CIRANO.
  • Handle: RePEc:cir:cirwor:2025s-15
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    File URL: https://cirano.qc.ca/files/publications/2025s-15.pdf
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    More about this item

    Keywords

    Machine learning; Nowcasting; Panel; Mixed-frequency; Fiscal indicators; Apprentissage automatique; Panel; Fréquences mixtes; Indicateurs budgétaires; Prévisions à court terme;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures

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