The predictive space or if x predicts y, what does y tell us about x?

A predictive regression for yt and a time series representation of the predictors, xt, together imply a univariate reduced form for yt. In this paper we work backwards, and ask: if we observe yt, what do its univariate properties tell us about any xt in the "predictive space" consistent with those properties? We provide a mathematical characterisation of the predictive space and certain of its derived properties. We derive both a lower and an upper bound for the R2 for any predictive regression for yt. We also show that for some empirically relevant univariate properties of yt, the entire predictive space can be very tightly constrained. We illustrate using Stock and Watson's (2007) univariate representation of inflation

Stambaugh correlations, monkey econometricians and redundant predictors

We consider inference in a widely used predictive model in empirical finance. "Stambaugh Bias" arises when innovations to the predictor variable are correlated with those in the predictive regression. We show that high values of the "Stambaugh Correlation" will arise naturally if the predictor is actually predictively redundant, but emerged from a randomised search by data mining econometricians. For such predictors even bias-corrected conventional tests will be severely distorted. We propose tests that distinguish well between redundant predictors and the true (or "perfect") predictor. An application of our tests does not reject the null that a range of predictors of stock returns are redundant

The limits to stock return predictability

We examine predictive return regressions from a new angle. We ask what observable univariate properties of returns tell us about the “predictive space” that defines the true predictive model: the triplet ¡ λ, R2 x, ρ¢ , where λ is the predictor’s persistence, R2 x is the predictive R-squared, and ρ is the "Stambaugh Correlation" (between innovations in the predictive system). When returns are nearly white noise, and the variance ratio slopes downwards, the predictive space can be tightly constrained. Data on real annual US stock returns suggest limited scope for even the best possible predictive regression to out-predict the univariate representation, particularly over long horizons

The Value of Citizen Scientists: Data Collection for American Eel Using Non-Traditional Field Gear & Social Media

American Eel (Anguilla rostrata) is a facultative catadromous species with a unique and complex life history. After hatching, larval eel begin their journey as leptocephalus in the Sargasso Sea and drift on ocean currents along the Atlantic coast, Gulf of Mexico, and Central and South America. They transform into glass eel as they approach shore and begin to develop pigment as they settle in estuaries or move upstream into rivers as elvers. American Eel then spend 3-40+ years in these habitats as yellow eel until they sexually mature into silver eel and return to the Sargasso Sea where they spawn and presumably die. State and federal agencies, multiple universities and numerous citizen science volunteers are working to better understand their movement patterns and recruitment window in Texas. Citizen scientists with coastal chapters of the Texas Master Naturalists (TMN) have taken a lead role in assisting with this effort. Since February of 2018, TMN have established a network of monitoring sites across the mid to upper Texas Coast to sample for juvenile American Eel using eel mops. Eel mops have been deployed for various lengths of time at 29 sites throughout the past two years and checked routinely for glass and elver eel. Volunteers have conducted approximately 250 eel mop checks and provided record of their catch by category (e.g., eel, shrimp, crab, other fish, etc.) based on occurrence or abundance. TMN have documented close to 7,000 individuals across all categories with various species of crab, shrimp, and fish being the most common groups collected. While no glass or elver eel have been collected in an eel mop, TMN have provide valuable data for this project by testing a common gear type that is often used to monitor for American Eel on the Atlantic Coast.Integrative Biolog

Nocturnal Pollination in Fruit Agriculture

Insect pollination in agriculture provides as much as 35% of the global food supply and contributes hundreds of billions of dollars to the global economy each year. In the past 30 years, reports of declining populations of managed and wild bees, notably the western honey bee (Apis mellifera) and a wide array of bumble bees (Bombus spp.), have raised concerns about the stability and outlook of agriculture. At the same time, agricultural dependence on insect pollinators is increasing as greater percentages of land being converted to pollinator-dependent crops, such as soy and oil palm, than pollinator-independent crops, such as grains and oats. Current knowledge of animal-mediated pollination in agriculture is focused on diurnal pollinators, with particular attention given to bees. Nocturnal insects, especially moths, represent a significant source of pollinator diversity, even greater than that of all diurnal pollinators combined. They are also well-known for their pollination services outside of agriculture. As such, these insects could offer valuable pollination services to agriculture, potentially providing additional stability and security to production. In this collection of works, I examine the roles of nocturnal-insect pollinators to fruit agriculture. The primary question was whether or not nocturnal pollinators offer any benefit to the production of selected fruits. I then examined which insects may be responsible for the observed pollination services. I found that nocturnal pollinators do not provide significant increases to the production peaches or muscadines. However, nocturnal pollinators significantly increased apple fruit set by comparison to a negative control, and nocturnal pollination levels were similar to those of diurnal pollinators. The most likely nocturnal pollinators were moths. These results are unprecedented and provide a new, potentially greatly underestimated, pollination system that demands immediate study

The Agreeableness of Conscientiousness: The Effects of Personality Facets on the Curvilinear Citizenship Behavior and Task Performance Relationship

Recent research has highlighted that the relationship between organizational citizenship behavior and task performance is nonlinear such that the occurrence of task performance behaviors will decrease as more time and resources are devoted to organizational citizenship behaviors. This occurs because of the restrictions of resource allocation theory which posit that employee resources are finite and limited, thus, there will be some tradeoff between engaging in various performance behaviors. The current study examined the potential moderating effect of specific facets of agreeableness, conscientiousness, and their interaction. Results showed that, as employees engaged in more individually focused citizenship behaviors, increasing levels of agreeableness increased the rate at which task performance decreased. When employees were high in both agreeableness and conscientiousness, task performance showed a linear relationship with organizationally focused citizenship behaviors. Agreeableness showed a direct negative effect on task performance, but had positive effects when mediated through job satisfaction and OCB. Conscientiousness had a direct positive effect on task performance, but showed negative effects when mediated through job satisfaction. Future research directions and implications are discussed