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

New Monetarist Economics: models

The purpose of this paper is to discuss some of the models used in New Monetarist Economics, which is our label for a body of recent work on money, banking, payments systems, asset markets, and related topics. A key principle in New Monetarism is that solid microfoundations are critical for understanding monetary issues. We survey recent papers on monetary theory, showing how they build on common foundations. We then lay out a tractable benchmark version of the model that allows us to address a variety of issues. We use it to analyze some classic economic topics, like the welfare effects of inflation, the relationship between money and capital accumulation, and the Phillips curve. We also extend the benchmark model in new ways, and show how it can be used to generate new insights in the study of payments, banking, and asset markets.Money ; Monetary policy

The Effect of a Screening Protocol on Opioid Use: An Integrative Review

The opioid crisis is a pervasive and social problem in the United States. Since 2001 several hundred thousand people have died from the misuse of prescription and illicit opioids. On average nearly 130 Americans perish every day due to opioid abuse while millions annually struggle with morbidity derived from opioid abuse disorders. This crisis causes tremendous physical and emotional suffering and death and is likely the most profound public health crisis our nation has faced. In 2015 alone, 52,000 people died of drug overdoses, with over 30,000 of those dying from opioid drugs. If left unchecked, the epidemic will continue to increase, and more of the population will continue to be affected by the opioid abuse. Literature related to opioid abuse is vast and expansive. However, the literature is lacking in the area of screening during the initial assessment to indicate the abuse potential. Findings derived from the literature show consistent support in the need for methodologies and interventions that prompt intervention or assist providers in the assessment of patients requiring opioids for management of chronic pain with the result to stalemate the opioid abuse in society. With this in mind, the purpose of this project is to determine whether the use of an opioid screening tool at the time of initial assessment of patients with chronic non-cancer pain will decrease the use of opioid use

Tobin's Q, asymmetric Information and aggregate stock market valuations

Estimates of Tobin’s Q for the United States using publicly available data present an apparent puzzle: it is systematically less than unity. This paper sets out a simple model consistent with rational stock market valuation under conditions of asymmetric information that provides a possible explanation of this puzzle

Detection of lipoarabinomannan (LAM) in urine is indicative of disseminated TB with renal involvement in patients living with HIV and advanced immunodeficiency: evidence and implications.

TB is the leading cause of HIV/AIDS-related deaths globally. New diagnostic tools are urgently needed to avert deaths from undiagnosed HIV-associated TB. Although simple assays that detect lipoarabinomannan (LAM) in urine have been commercially available for years, their specific role and utility were initially misunderstood, such that they have been slower to emerge from the diagnostics pipeline than otherwise might have been expected. In this article, we review and explain how urine-LAM assays should be understood as diagnostics for disseminated TB in HIV-positive patients with advanced immunodeficiency, in whom haematogenous TB dissemination to the kidneys serves as the primary mechanism by which LAM enters the urine. These insights are critical for the appropriate design of studies to evaluate these assays and to understand how they might be most usefully implemented. This understanding also supports the 2015 WHO recommendations on the restricted use of these assays in sick HIV-positive patients with advanced immunodeficiency

R2 Bounds for Predictive Models: What Univariate Properties Tell us About Multivariate Predictability

A long-standing puzzle in macroeconomic forecasting has been that a wide variety of multivariate models have struggled to out-predict univariate models consistently. We seek an explanation for this puzzle in terms of population properties. We derive bounds for the predictive R2 of the true, but unknown, multivariate model from univariate ARMA parameters alone. These bounds can be quite tight, implying little forecasting gain even if we knew the true multivariate model. We illustrate using CPI inflation data