  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.
