Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
This addresses a finite-sample bias issue in econometrics for researchers estimating average treatment effects, though it appears incremental as an improvement over an existing method.
The paper tackles the problem of omitted variable bias in high-dimensional linear regression estimation, proposing Post-Double-Autometrics as an alternative to Post-Double-Lasso, and shows it outperforms the latter in a standard economic growth application, providing new insights into convergence hypotheses.
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.