  In a variety of different settings cumulative sum (CUSUM) procedures have
been applied for the sequential detection of structural breaks in the
parameters of stochastic models. Yet their performance depends strongly on the
time of change and is best under early-change scenarios. For later changes
their finite sample behavior is rather questionable. We therefore propose
modified CUSUM procedures for the detection of abrupt changes in the regression
parameter of multiple time series regression models, that show a higher
stability with respect to the time of change than ordinary CUSUM procedures.
The asymptotic distributions of the test statistics and the consistency of the
procedures are provided. In a simulation study it is shown that the proposed
procedures behave well in finite samples. Finally the procedures are applied to
a set of capital asset pricing data related to the Fama-French extension of the
capital asset pricing model.
