Exploring Hoover and Perez's experimental designs using global sensitivity analysis

TL;DR

This paper applies global sensitivity analysis to variable selection in regression, demonstrating significant improvements over Hoover and Perez's original algorithms in recovering true model specifications.

Contribution

It introduces a novel combination of global sensitivity analysis with existing algorithms for regression variable selection, enhancing recovery accuracy.

Findings

Global sensitivity analysis improves variable recovery rates.

The combined approach outperforms Hoover and Perez's original algorithms.

Significant enhancement in identifying true model specifications.

Abstract

This paper investigates variable-selection procedures in regression that make use of global sensitivity analysis. The approach is combined with existing algorithms and it is applied to the time series regression designs proposed by Hoover and Perez. A comparison of an algorithm employing global sensitivity analysis and the (optimized) algorithm of Hoover and Perez shows that the former significantly improves the recovery rates of original specifications.