Since the 1970s, the characteristics of international business cycles have changed, and deeper economic integration has modified the features of cross-country comovement. We formally test for correlation shifts in measures of real economic activity and economic/financial integration. In Europe we find some statistically significant evidence of higher correlations for several subgroups of countries following the creation of the EMU in 1999. We detect significantly more pronounced correlations between Mexico and the United States and between Mexico and Canada in North America after the enforcement of the NAFTA in 1994. Results are derived from an econometric framework based on nonparametric iterated stationary bootstrap methods, whose statistical reliability and performance we assess through Monte Carlo simulations.

Resistance to anthelmintics in gastrointestinal nematodes of livestock is a serious problem and appropriate methods are required to identify and quantify resistance. However, quantification and assessment of resistance depend on an accurate measure of treatment efficacy, and current methodologies fail to properly address the issue. The fecal egg count reduction test (FECRT) is the practical gold standard for measuring anthelmintic efficacy on farms, but these types of data are fraught with high variability that greatly impacts the accuracy of inference on efficacy. This paper develops a statistical model to measure, assess, and evaluate the efficacy of the anthelmintic treatment on horse farms as determined by FECRT. Novel robust bootstrap methods are developed to analyse the data and are compared to other suggested methods in the literature in terms of Type I error and power. The results demonstrate that the bootstrap methods have an optimal Type I error rate and high power to detect differences between the presumed and true efficacy without the need to know the true distribution of pre-treatment egg counts. Finally, data from multiple farms are studied and statistical models developed that take into account between-farm variability. Our analysis establishes that if inter-farm variability is not taken into account, misleading conclusions about resistance can be made.

It is argued that standard impulse response analysis based on vector autoregressive models has a number of shortcomings. Although the impulse responses are estimated quantities, measures for sampling variability such as confidence intervals sometimes are not provided. If confidence intervals are given, they often are based on bootstrap methods with dubious theoretical properties. These problems are illustrated using two German monetary systems. Proposals are made for improving current practice. Special emphasis is placed on systems with cointegrated variables.