The paper presents an approach to the analysis of data that contains (multiple) structural changes in a linear regression setup. We implement various strategies which have been suggested in the literature for testing against structural changes as well as a dynamic programming algorithm for the...

Time series analysis is an important and complex problem in machine learning and statistics. Real-world applications can consist of very large and high dimensional time series data. Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM...

This paper is concerned with testing the null hypothesis of no cointegration among I(1) variables when the cointegration residuals are I(d) with 0 < d <1. This possibility is entertained with increasing frequency in many applications, (see e.g. Cheung and Lai 1993 Baillie and Bollerslev 1994 Booth and Tse 1995 or Baillie 1996 for examples. We consider the power of various cointegration tests both for the stationary case d < 5 and for the nonstationary case d > 5.

The paper considers tests against for autocorrelation among the disturbances in linear regression models that can be expressed as ratios of quadratic forms. It shows that such tests are in general not unbiased and that power can even drop to zero for certain regressors and spatial weight...

The recent renaissance of business cycle analysis has led to a renewed interest in business cycle classification as pioneered by Burns/Mitchell, Spiethoff and resumed in the seventies by Meyer/Weinberg (1975a, b). The rather successful elaboration and test of a “modern” four-phase...

The paper discusses structural change as possible mechanism that generates the appearance of long memory in economic time series. It shows that there are no long memory effects in German stock returns and that long memory in squares of German stock returns disappears once shifting means are...

The properties of Cpmk in the presence of asymmetric specification limits are discussed. It is shown that Cpmk tends to zero as the process variation increases and vice versa. Furthermore, if the process variation is small, Cpmk has its maximum near the target value but the maximum moves towards...

In this paper, we examine the German business cycle (from 1955 to 1994) in order to identify univariate and multivariate outliers as well as influence points corresponding to Linear Discriminant Analysis. The locations of the corresponding observations are compared and economically interpreted.

We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind...