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Linear Econometric Models with Machine Learning

In: Econometrics with Machine Learning

Author

Listed:
  • Felix Chan

    (Curtin University)

  • László Mátyás

    (Central European University)

Abstract

This chapter discusses some of the more popular shrinkage estimators in the machine learning literature with a focus on their potential use in econometric analysis. Specifically, it examines their applicability in the context of linear regression models. The asymptotic properties of these estimators are discussed and the implications on statistical inference are explored. Given the existing knowledge of these estimators, the chapter advocates the use of partially penalized methods for statistical inference. Monte Carlo simulations suggest that these methods perform reasonably well. Extensions of these estimators to a panel data setting are also discussed, especially in relation to fixed effects models.

Suggested Citation

  • Felix Chan & László Mátyás, 2022. "Linear Econometric Models with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 1-39, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-15149-1_1
    DOI: 10.1007/978-3-031-15149-1_1
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    Citations

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    Cited by:

    1. Alina Landowska & Robert A. K{l}opotek & Dariusz Filip & Konrad Raczkowski, 2025. "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest," Papers 2504.20993, arXiv.org.
    2. Adam, Nasir, 2026. "Farmers’ Preferences for fossil-free mineral nitrogen fertilisers: prices, production origin, climate and identity," 100th Annual Conference, March 23-25, 2026, Wadham College, University of Oxford, Oxford, UK 397891, Agricultural Economics Society (AES).
    3. Ölkers, Tim & Liu, Shuang & Mußhoff, Oliver, 2023. "A typology of Malian farmers and their credit repayment performance - An unsupervised machine learning approach," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334547, Agricultural Economics Society - AES.
    4. Felix Chan & Les Oxley, 2023. "A pulse check on recent developments in time series econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 3-6, February.
    5. Tim Ölkers & Shuang Liu & Oliver Mußhoff & Xiaohua Yu, 2025. "Patterns and heterogeneity in credit repayment performance: Evidence from Malian farmers," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 47(2), pages 694-722, May.
    6. Millimet, Daniel L. & Bellemare, Marc, 2023. "Fixed Effects and Causal Inference," IZA Discussion Papers 16202, IZA Network @ LISER.
    7. Anna Rita Dipierro & Fernando Jimenéz Barrionuevo & Pierluigi Toma, 2025. "Predicting ESG Controversies in Banks Using Machine Learning Techniques," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 32(3), pages 3525-3544, May.

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