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A general class of fluctuation tests for parameter instability in an M‐estimation framework is suggested. Tests from this framework can be constructed by first choosing an appropriate estimation technique, deriving a partial sum process of the estimation scores that captures instabilities over time, and aggregating this process to a test statistic by using a suitable scalar functional. Inference for these tests is based on functional central limit theorems, which are derived under the null hypothesis of parameter stability and local alternatives. For (generalized) linear regression models, concrete tests are derived, which cover several known tests for (approximately) normal data but also allow for testing for parameter instability in regressions with binary or count data. The usefulness of the test procedures—complemented by powerful visualizations derived from these—is illustrated using Dow Jones industrial average stock returns, youth homicides in Boston, USA, and illegitimate births in Grossarl, Austria.

In this article we explore the semantic space spanned by self-reported statements of Republican voters. Our semantic structure analysis uses multidimensional scaling and social network analysis to extract, explore, and visualize word patterns and word associations in response to the stimulus statement "I'm a Republican, because ." which were collected from the official website of the Republican Party. With psychological value theory as our backdrop, we examine the association of specific keywords within and across the statements, compute clusters of statements based on these associations, and explore common word sequences Republican voters use to characterize their political association with the Party. (authors' abstract)

The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book enables the reader to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R

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 dating of the breakpoints in the R statistical software package. Using historical data on Nile river discharges, road casualties in Great Britain and oil prices in Germany it is shown that changes in the mean of a time series as well as in the coefficients of a linear regression are easily matched with identifiable historical, political or economic events.

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 dating of the breakpoints in the R statistical software package. Using historical data on Nile river discharges, road casualties in Great Britain and oil prices in Germany it is shown that changes in the mean of a time series as well as in the coefficients of a linear regression are easily matched with identifiable historical, political or economic events.