  We analyze the extreme value dependence of independent, not necessarily
identically distributed multivariate regularly varying random vectors. More
specifically, we propose estimators of the spectral measure locally at some
time point and of the spectral measures integrated over time. The uniform
asymptotic normality of these estimators is proved under suitable nonparametric
smoothness and regularity assumptions. We then use the process convergence of
the integrated spectral measure to devise consistent tests for the null
hypothesis that the spectral measure does not change over time. The finite
sample performance of these tests is investigated in Monte Carlo simulations.
