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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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