A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS)
test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve
two distinct purposes. Initially, the test seeks to determine whether there exists a
statistically significant difference between the distribution of forecast errors, and
secondly it exploits the principles of stochastic dominance to determine whether the
forecasts with the lower error also reports a stochastically smaller error than forecasts
from a competing model, and thereby enables distinguishing between the predictive accuracy
of forecasts. KSPA test has been described in : Hassani and Silva (2015)