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

Sullivan Hué, Sébastien Laurent, Ulrich Aiounou, Emmanuel Flachaire

arXiv 26 Nov 2025 · Econometrics

arXiv:2511.21257 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

33
references
52
in-text mentions
33
distinct cited
1
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8,793
main-text words

appendix boundary found by appendix_titled_section at “Appendix 1: Name and description of the variables of the application on growth data” · 90% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Wüthrich, K. and Y. Zhu (2023) Omitted variable bias of Lasso-based inference methods: A finite sample analysis0.92843100%
2Belloni, A. and V. Chernozhukov (2011) L1-penalized quantile regression in high-dimensional sparse models0.81142100%
3Belloni, A., V. Chernozhukov, and C. Hansen (2013) Inference for high-dimensional sparse econometric models0.73732100%
4Hendry, D. F. and J. A. Doornik (2014) Empirical model discovery and theory evaluation: automatic selection methods in econometrics0.73732100%
5Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls0.73732100%
6Doornik, J. A (2009) Autometrics0.73732100%
7Barro, R. J. and J.-W. Lee (1994) Data set for a panel of 138 countries0.64422100%
8Hendry, D. F (1980) Econometrics: alchemy or science?0.64422100%
9Barro, R. and X. Sala-i Martin (1995) Economic Growth0.51121100%
10Hendry, D. F. and S. Johansen (2011) The properties of model selection when retaining theory variables0.51121100%

Showing the top 10 of 33 scored citations.