Spencer Marlen-Starr 
This Research Report was written as a Graduate Student in the Data Analytics Engineering Master’s Program at George Mason University under the supervision of the Econometrician…

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This article proposes the Estimated Exhaustive Regression (EER) procedure as a computationally feasible version of the Exhaustive Regression (ER) algorithm for variable selection in linear regression analysis. The EER algorithm is compared to other benchmark variable selection algorithms, including LASSO and Backward Elimination and Forward Selection variations of Stepwise Regression. The results show that EER has a lower False Negative Rate and comparable runtime to the benchmarks, making it a powerful tool for variable selection.