Sensitivity analysis is important for its own sake and also in combination with
diagnostic testing. We consider the question how to use sensitivity statistics in
practice, in particular how to judge whether sensitivity is large or small. For this
purpose we distinguish between absolute and relative sensitivity and highlight the
context-dependent nature of any sensitivity analysis. Relative sensitivity is then
applied in the context of forecast combination and sensitivity-based weights are
introduced. All concepts are illustrated through the European yield curve. In this
context it is natural to look at sensitivity to autocorrelation and normality assumptions.
Different forecasting models are combined with equal, fit-based and sensitivity-based
weights, and compared with the multivariate and random walk benchmarks. We show
that the fit-based weights and the sensitivity-based weights are complementary. For
long-term maturities the sensitivity-based weights perform better than other weights.