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Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso

Author

Listed:
  • Sullivan Hu'e
  • S'ebastien Laurent
  • Ulrich Aiounou
  • Emmanuel Flachaire

Abstract

Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.

Suggested Citation

  • Sullivan Hu'e & S'ebastien Laurent & Ulrich Aiounou & Emmanuel Flachaire, 2025. "Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso," Papers 2511.21257, arXiv.org.
    • Handle: RePEc:arx:papers:2511.21257
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    References listed on IDEAS

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    1. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "hdm: High-Dimensional Metrics," CeMMAP working papers 37/16, Institute for Fiscal Studies.
    4. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    5. David F. Hendry & Søren Johansen, 2011. "The Properties of Model Selection when Retaining Theory Variables," CREATES Research Papers 2011-36, Department of Economics and Business Economics, Aarhus University.
    6. repec:bla:econom:v:47:y:1980:i:188:p:387-406 is not listed on IDEAS
    7. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    8. Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1350-1361, July.
    9. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2023. "Robust Discovery of Regression Models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 31-51.
    10. Kaspar Wüthrich & Ying Zhu, 2023. "Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 982-997, July.
    11. Wüthrich, Kaspar & Zhu, Ying, 2023. "Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis," University of California at San Diego, Economics Working Paper Series qt1gp6g9gm, Department of Economics, UC San Diego.
    12. Søren Johansen & Bent Nielsen, 2016. "Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 321-348, June.
    13. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2024. "Interpretable Machine Learning Using Partial Linear Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 519-540, June.
    14. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    15. Søren Johansen & Bent Nielsen, 2016. "Rejoinder: Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 374-381, June.
    16. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    17. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    18. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
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