Machine Learning and Shrinkage in Dynamic Panel Forecasting

Magdalena Cornejo () and Walter Sosa Escudero ()
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Magdalena Cornejo: Universidad Torcuato Di Tella - CONICET
Walter Sosa Escudero: Universidad de San Andrés - CONICET

No 183, Working Papers from Universidad de San Andres, Departamento de Economia

Abstract: This paper studies forecasting in dynamic panel data models with fixed effects. We compare the forecasting accuracy of conventional estimators—pooledOLS,fixed effects, Anderson–Hsiao, and Arellano–Bond—against shrinkage and regularization methods such as Ridge, LASSO, ElasticNet, empirical Bayes maximum likelihood and the recent unbiased risk estimation of Kwon (2026). Monte Carlo evidence shows that shrinkage methods substantially improve out-of-sample accuracy. An empirical application to firm-level leverage dynamics using Compustat data confirms the relevance of these findings for forecasting in corporate finance. Machine learning regularization can improve forecasting performance in dynamic panel settings while preserving the structural framework.

Keywords: Forecasting; Dynamic panel data; Machine learning; Regularization; Corporate finance. (search for similar items in EconPapers)
JEL-codes: C53 C58 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2026-05, Revised 2026-05
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https://repec.udesa.edu.ar/pub/econ/doc183.pdf First version, May 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:sad:wpaper:183

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