Endurance exercise and leg strength in older women

Quadriceps strength and mass peak in the third decade of life, plateau, and then decline from the fifth decade on. To examine the influence of chronic endurance training and age on lean mass and leg strength, women runners (n = 62, age 43-69 years) and sedentary participants (n = 33, age 43-66 years) were divided into 40-, 50-, and 60-year age groups. Absolute isokinetic concentric torque did not differ between runners and sedentary women (97.9 +/- 19.5 and 104.6 +/- 22.7 N . m, respectively, p = .18) but was different between age groups independent of exercise status (107.6 +/- 18.4, 97.1 +/- 19.9, and 90.1 +/- 21.4 N . m, for 40s, 50s, and 60s, respectively, p < .05). Lean body mass also differed by age group (p < .05) but did not change differently among runners and sedentary women. These findings suggest that chronic endurance training might not influence the loss of muscle mass and muscle strength that occur with aging.     

A Bootstrap Stationarity Test for Predictive Regression Invalidity

In order for predictive regression tests to deliver asymptotically valid inference, account has to be taken of the degree of persistence of the predictors under test. There is also a maintained assumption that any predictability in the variable of interest is purely attributable to the predictors under test. Violation of this assumption by the omission of relevant persistent predictors renders the predictive regression invalid, and potentially also spurious, as both the finite sample and asymptotic size of the predictability tests can be significantly inflated. In response, we propose a predictive regression invalidity test based on a stationarity testing approach. To allow for an unknown degree of persistence in the putative predictors, and for heteroscedasticity in the data, we implement our proposed test using a fixed regressor wild bootstrap procedure. We demonstrate the asymptotic validity of the proposed bootstrap test by proving that the limit distribution of the bootstrap statistic, conditional on the data, is the same as the limit null distribution of the statistic computed on the original data, conditional on the predictor. This corrects a long-standing error in the bootstrap literature whereby it is incorrectly argued that for strongly persistent regressors and test statistics akin to ours the validity of the fixed regressor bootstrap obtains through equivalence to an unconditional limit distribution. Our bootstrap results are therefore of interest in their own right and are likely to have applications beyond the present context. An illustration is given by reexamining the results relating to U.S. stock returns data in Campbell and Yogo (2006 Campbell, J. Y. and Yogo, M. (2006), “Efficient Tests of Stock Return Predictability,” Journal of Financial Economics, 81, 27–60.[Crossref], [Web of Science ®] , [Google Scholar]). Supplementary materials for this article are available online.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

KIDS OR CASH? EXPLORING CHARTER SCHOOL RESPONSES TO DECLINING GOVERNMENT REVENUES

While the literature is extensive on school districts' revenue sources, less research has been done on the impact of donations on school district funds. In this paper, we extend the theoretical literature on crowding out of private donations by government grants for one type of nonprofit firm, namely charter schools. The theoretical model leads us to focus on the key relationships among fundraising effort, enrollment (which is tied to federal and state funding) and donations. Using a dataset on Texas charter schools we adopt a two-stage approach to examine the empirical relationship between changes in nondonor revenues and the donations received by charter schools. Like the extensive empirical estimates of the effects of government grants on donations for other types of nonprofit firms, we find evidence of crowding-out with respect to our sample of charter schools. We also find a significant, positive effect of fundraising on donations with a $1 increase in fundraising associated to a $0.58 increase in donations, a pattern consistent with overinvestment in fundraising. Enrollments exhibit a robust inverse relationship to changes in nondonor revenues. (JEL H00, H32, H50)     

Fiddling while the planet burns? COP25 in perspective

ABSTRACT

With fires, storms, social protests, and climate strikes sweeping the world, 2019 should have been a tipping point in how the world responds to global heating. This was the backdrop to the COP25 climate change summit which took place in Madrid in December 2019. This paper assesses the outcomes of the meeting and the path towards the critically important meeting in Glasgow at the end of 2020. It analyses and explains the key points of contention over levels of ambition, the rules which should govern global carbon markets and sensitive issues such as loss and damage associated with the impacts of climate change. The analysis is situated within a broader geopolitical and economic context of right-wing populism, deepening forms of marketization and financialization of responses to climate change and against a background of a world increasingly feeling the effects of the climate crisis.     

Reality check. Evaluating a school-based gang prevention model

The Gang Resistance Education and Training (G.R.E.A.T.) program is a school-based gang prevention initiative developed in 1991 through the collaborative efforts of the Bureau of Alcohol, Tobacco, and Firearms, the Federal Law Enforcement Training Center, and the Phoenix Police Department. Uniformed law enforcement officers, certified as G.R.E.A.T. instructors, teach the 9-week curriculum to middle students. In 1994, the National Institute of Justice funded a national evaluation of the G.R.E.A.T. program. The process evaluation component of this larger study is reported. First, results of on-site observations of the G.R.E.A.T. Officer Training program, including an overview of the training activities, and the authors' assessment of the training process are reported. Second, observations of the implementation of the program by officers at six sites are reported. Of primary concern was whether the program delivered to students was similar to the program taught to the officers during the G.R.E.A.T. Officer Training.     

Modelling Variability in the Branching Structure of Strawberry Inflorescences

The branching structure of inflorescences of the cultivated strawberry ( Fragaria × ananassa Duch.) is very variable. This paper demonstrates that some aspects of this variability are well described by a simple stochastic model of branching that has two adjustable parameters. The model is shown to provide a good fit to data from a set of almost 700 inflorescences of the cultivar Elsanta, collected over two successive years. For one parameter the maximum likelihood estimator is a moment estimator which is fully efficient even if the detailed branching structure of the inflorescences is not recorded. This parameter provides a convenient summary of branching vigour. The maximum likelihood estimator of the second parameter must be determined iteratively and can be quite inefficient unless the full branching structure is recorded. The model demonstrates that branching structure is affected by the order in which inflorescences emerge on the plant.