Preliminary. AbstractThe local asymptotic power of many popular non-cointegration tests has recently been shown to depend on a certain nuisance parameter. Depending on the value of that parameter, different tests perform best. This paper suggests combination procedures with the aim of providing meta tests that maintain high power across the range of the nuisance parameter. The local asymptotic power of the new meta tests is in general almost as high as that of the more powerful of the underlying tests. When the underlying tests have similar power, the meta tests are even more powerful than the best underlying test. At the same time, our new meta tests avoid the arbitrary decision which test to use if single test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our test to 161 data sets from published cointegration studies. There, in one third of all cases single tests give conflicting results whereas our meta tests provide an unambiguous test decision.

Preliminary. AbstractThe local asymptotic power of many popular non-cointegration tests has recently been shown to depend on a certain nuisance parameter. Depending on the value of that parameter, different tests perform best. This paper suggests combination procedures with the aim of providing meta tests that maintain high power across the range of the nuisance parameter. The local asymptotic power of the new meta tests is in general almost as high as that of the more powerful of the underlying tests. When the underlying tests have similar power, the meta tests are even more powerful than the best underlying test. At the same time, our new meta tests avoid the arbitrary decision which test to use if single test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our test to 161 data sets from published cointegration studies. There, in one third of all cases single tests give conflicting results whereas our meta tests provide an unambiguous test decision.

This article proposes a new panel unit root test based on Simes ' (1986) classical intersection test. The test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring p-values of time series unit root tests of the series in the panel, and no resampling. Monte Carlo experiments show good size and power properties relative to existing panel unit root tests. Unlike previously suggested tests, the new test allows to identify the units in the panel for which the alternative of stationarity can be said to hold. We provide an empirical application to real exchange rate data.

BackgroundWe determined body weight increase in first year Dutch college students. We had the objective to determine whether the awareness of the unhealthy lifestyle raised concerns and willingness to change habits.MethodsBody weight, heartbeat, BMI, body fat percentages, and blood pressure values were collected from 1095 students. Comprehensive statistical analysis was performed on the data.ResultsThe students had a mean weight gain of 1.1 kg and an average BMI gain of 0.35. Members of a student corps gained significantly more weight (1.6 ± 3.1 kg) than non-members (1.0 ± 2.5 kg), while students who are living independently gained an average of 0.5 kg more than students living with their parents (p < 0.05). Approximately 40% of the students changed their eating patterns and 30.7% of the students consumed more alcohol.ConclusionsStudents experienced hindrance in physical exercise and mental well-being. Students with a high BMI without irregular eating habits were willing to change their lifestyle. However, students who had irregular lifestyles exhibited the lowest willingness to change their eating behaviors and to lose weight. Our study provides insight into means by which adolescents at high risk for weight gain can be approached to improve experienced quality of life.