Limits to growth rates in an ethereal economy

It has been argued that economic growth can continue despite the finite nature of the Earth and its ecological systems if growth is concentrated in an ethereal economy where ideas and information dominate over physical inputs. In this paper, we agree that in a sustainable society continued growth must eventually be concentrated in the ethereal economy; however, we argue that such growth cannot occur at the ongoing exponential rate that currently underpins the constant rate of returns relied upon within our economies. As there is a limit to how fast a population can adopt new ideas, and as such adoption and innovation itself occurs in unpredictable bursts, growth in an ethereal economy will follow a model of punctuated equilibrium composed of exponential bursts, logistic growth, and stable/stagnating periods in a manner similar to ecological evolutionary processes. Although such an economic environment is likely far in the future, lessons in not overtaxing ecological capital and encouraging information dissemination and knowledge diffusion are applicable to problems we face today.     

Bayesian Estimation of Prediction Error and Variable Selection in Linear Regression

An important statistical application is the problem of determining an appropriate set of input variables for modelling a response variable. In such an application, candidate models are characterized by which input variables are included in the mean structure. A reasonable approach to gauging the propriety of a candidate model is to define a discrepancy function through the prediction error associated with this model. An optimal set of input variables is then determined by searching for the candidate model that minimizes the prediction error. In this paper, we focus on a Bayesian approach to estimating a discrepancy function based on prediction error in linear regression. It is shown how this approach provides an informative method for quantifying model selection uncertainty.     

Communicating Environmental Information: Are Marketing Claims on Packaging Misleading?

The increased usage of questionable environmental marketing claims has become an issue of concern for academics, policy makers and consumers. Much of the research to date, has focused on the accuracy of environmental claims in advertisements, with the information on product packaging being largely ignored. This study uses a content analysis to examine the environmental information on packaging. More specifically it examines the packaging of the population of dishwashing liquid bottles available in grocery stores in a large city in Australia. Evaluation criteria are developed to classify the various types of information and the degree to which the information is "misleading". Seven different informational categories and four accuracy categories are developed. These criteria are developed based on the existing environmental advertising literature and environmental marketing regulations in the U.S. and Australia. It was found that a majority of the packaging information can be classified as being not accurate.     

Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression

Model selection criteria often arise by constructing unbiased or approximately unbiased estimators of measures known as expected overall discrepancies (Linhart & Zucchini, 1986, p. 19). Such measures quantify the disparity between the true model (i.e., the model which generated the observed data) and a fitted candidate model. For linear regression with normally distributed error terms, the "corrected" Akaike information criterion and the "modified" conceptual predictive statistic have been proposed as exactly unbiased estimators of their respective target discrepancies. We expand on previous work to additionally show that these criteria achieve minimum variance within the class of unbiased estimators.