Prispevek je nastal v soavtorstvu Philippa Gouleta Coulombea, Massimiliana Marcellina in Daliborja Stevanovića.

Povzetek

Authors 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. Authors 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 setting.

O predavatelju in soavtorju

Dalibor Stevanović je redni profesor, nosilec Katedre za makroekonomijo in napovedovanje ter sonosilec podiplomskih študijskih programov na Oddelku za ekonomijo Fakultete za management Univerze Québec v Montrealu (Université du Québec à Montréal). Profesor Stevanović opravlja raziskovalno delo tudi v več mednarodnih raziskovalnih središčih (CIRANO, Rimini Centre for Economic Analysis, Centre for Monetary and Financial Economics). Njegova raziskovalna področja obsegajo ekonometrijo časovnih vrst, makroekonomijo in finance, s posebnim poudarkom na strojnem učenju in analizi velikih podatkov. Svoje raziskovalne rezultate objavlja v vodilnih mednarodnih znanstvenih revijah.

Kako se prijaviti

Za prijavo na seminar pošljite e-pošto na naslov seminar@bsi.si. Prijavite se lahko do torka, 9. septembra 2026. Seminar bo potekal v angleškem jeziku.