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

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

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 impact of organic livestock standards on animal welfare – a questionnaire survey of advisors, inspectors and veterinarians

This report was presented at the UK Organic Research 2002 Conference. A questionnaire survey of organic sector body inspectors, organic advisors and farm animal veterinarians was conducted to examine the respondents’ perceptions of the ability of the organic standards to deliver positive impacts on welfare of organic livestock. A total of 44 separate standards concerning livestock production were extracted from the United Kingdom Register of Organic Food Production livestock production standards. The respondents were asked to consider the potential impact of each standard on animal welfare in comparison to the routine practices used on conventional farms, using a five-point scale (improve significantly, improve slightly, no impact, deteriorate slightly and deteriorate significantly). A simple scoring system was used to rank the different standards in terms of their perceived positive impact on animal welfare. The significance of differences between respondent groups and livestock species were examined. Preliminary findings of the survey are summarised and the usefulness of the approach to evaluate livestock production standards is discussed