  We consider a process $ X= (X_t)_{t\in \Z}$ belonging to a large class of
causal models including AR($\infty$), ARCH($\infty$), TARCH($\infty$),...
models.
  We assume that the model depends on a parameter $\theta_0 \in \R^d$ and
consider the problem of testing for change in the parameter. Two statistics
$\hat{Q}^{(1)}_n$ and $ \hat{Q}^{(2)}_n$ are constructed using quasi-likelihood
estimator (QLME) of the parameter. Under the null hypothesis that there is no
change, it is shown that each of these two statistics weakly converges to the
supremum of the sum of the squares of independent Brownian bridges.
  Under the local alternative that there is one change, we show that the test
statistic $\hat{Q}_n=\text{max} \big(\hat{Q}^{(1)}_n, \hat{Q}^{(2)}_n \big) $
diverges to infinity.
  Some simulation results for AR(1), ARCH(1) and GARCH(1,1) models are reported
to show the applicability and the performance of our procedure with comparisons
to some other approaches.
