We consider the finite sample power of various tests against serial correlation in the disturbances of a linear regression when these disturbances follow a stationary long memory process. It emerges that the power depends on the form of the regressor matrix and that, for the Durbin-Watson test...

We provide a method for distinguishing long-range dependence from deterministic trends such as structural breaks. The method is based on the comparison of standard log-periodogram regression estimation of the memory parameter with its tapered counterpart. The difference of these estimators...

We show that small trends do not influence log-periodogram based estimators for the memory parameter in a stationary invertible long-memory process. In the case of slowly decaying trends which are easily confused with long-range dependence we show by Monte Carlo methods that the tapered...

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation – given a history period for which a regression relationship is known to be...

The repeated median line estimator is a highly robust method for fitting a regression line to a set of n data points in the plane. In this paper, we consider the problem of updating the estimate after a point is removed from or added to the data set. This problem occurs e.g. in statistical...

Tests for shift detection in locally-stationary autoregressive time series are constructed which resist contamination by a substantial amount of outliers. Tests based on a comparison of local medians standardized by a highly robust estimate of the variability show reliable performance in a broad...

We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends...