Contributions of unrestricted funds: The donor organization–public relationship and alumni attitudes and behaviors

This exploratory pilot study examined alumni attitudes and behaviors toward making contributions in a nonprofit higher education organizational setting. The theoretical base of the study was that nonprofit higher education organizations operate in competitive marketing environments within a resource dependence open systems framework. In an effort to better understand alumni donor attitudes and behaviors in this context, this research explored the predictive relationship between the donor organization–public relationship (OPR) and alumni donor attitudes toward willingness to contribute unrestricted funds and donor behaviors concerning size of gifts made, frequency per year of contributions, and frequency of years of contributions using Huang's (2001) organization–public relationship assessment. Results indicate that donor OPR significantly predicts alumni donor attitudes toward willingness to contribute unrestricted funds. The results do not support that donor OPR significantly predicts size of gifts made, frequency per year of contributions, and frequency of years of contributions.     

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black-Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.     

Promoting sense-making in volatile environments: Developing resilience in disaster management

Over the past decade, the research on disaster administration has taken a critical look at the command and control operations in disaster response and recovery. Following the notable bureaucratic failures in response to Hurricane Katrina and the tsunami in Indonesia, researchers have focused on disaster administration system. The various actors within the system should focus on sense-making and resilience to be agile in the face of catastrophic events. This study profiles the contributions made through a focus on developing resilience in disaster management. Through a detailed literature review in multiple areas of the disaster management literature, along with illustrative examples from the challenges that agents have faced in several recent global disasters from 2014 to the present, the importance of resilience is highlighted. Areas for future research are included.     

Looking After Children: implications for social work education

Pilot schemes demonstrated a wide range of areas in which the Looking After Children materials might be used in social work training, both as part of the academic and the practice curriculum.     

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

ABSTRACT

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. Supplementary materials for this article are available online.