In statistical practice multicollinearity of predictor variables is rather the rule than the exception and appropriate models are needed to avoid instability of predictions. Feature extraction methods reflect the idea that latent variables not measurable directly are underlying the original...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not...

Data revisions in macroeconomic time series are typically studied in isolation ignoring the joint behaviour of revisions across different series. This ignores (i) the possibility that early releases of some series may help forecast revisions in other series and (ii) the problems statistical...

Many multivariate methods that are apparently distinct can be linked by introducing one or more parameters in their definition. Methods that can be linked in this way are correspondence analysis, unweighted or weighted logratio analysis (the latter also known as quot;spectral mappingquot;),...

Missing data is a problem appearing ubiquitously across many fields and needs to be dealt with systematically. For multivariate time series data imputation can be a challenging problem. We consider the particular case of credit default swap time series, where missing data can pose a considerable...

This manuscript is the author's response to: "Dochtermann, N.A. & Jenkins, S.H. Multivariate methods and small sample sizes, Ethology, 117, 95-101." and accompanies this paper: "Budaev, S. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines....