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Machine Learning and Shrinkage in Dynamic Panel Forecasting

Author

Listed:
  • Magdalena Cornejo

    (Universidad Torcuato Di Tella - CONICET)

  • Walter Sosa Escudero

    (Universidad de San Andrés - CONICET)

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.

Suggested Citation

  • Magdalena Cornejo & Walter Sosa Escudero, 2026. "Machine Learning and Shrinkage in Dynamic Panel Forecasting," Working Papers 183, Universidad de San Andres, Departamento de Economia, revised May 2026.
    • Handle: RePEc:sad:wpaper:183
    as

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    File URL: https://repec.udesa.edu.ar/pub/econ/doc183.pdf
    File Function: First version, May 2026
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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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