Specifying the Forecast Generating Process for Exchange Rate Survey Forecasts

This paper contributes to the literature on the modeling of survey forecasts using learning variables. We use individual industry data on yen-dollar exchange rate predictions at the two week, three month, and six month horizons supplied by the Japan Center for International Finance. Compared to earlier studies, our focus is not on testing a single type of learning model, whether univariate or mixed, but on searching over many types of learning models to determine if any are congruent. In addition to including the standard expectational variables (adaptive, extrapolative, and regressive), we also include a set of interactive variables which allow for lagged dependence of one industry’s forecast on the others. Our search produces a remarkably small number of congruent specifications-even when we allow for 1) a flexible lag specification, 2) endogenous break points and 3) an expansion of the initial list of regressors to include lagged dependent variables and use a General-to-Specific modeling strategy. We conclude that, regardless of forecasters’ ability to produce rational forecasts, they are not only “different,” but different in ways that cannot be adequately represented by learning models.Learning Models, Exchange Rate, Survey Forecasts

To Aggregate, Pool, or Neither: Testing the Rational Expectations Hypothesis Using Survey Data

It is well known that even if all forecasters are rational, estimated coefficients in unbiasedness regressions using consensus forecasts are inconsistent because forecasters have private information. However, if all forecasters face a common realization, pooled estimators are also inconsistent. In contrast, we show that when predictions and realizations are integrated and cointegrated, micro-homogeneity ensures that consensus and pooled estimators are consistent. Therefore, contrary to claims in the literature, in the absence of micro-homogeneity, pooling is not a solution to the aggregation problem. We reject micro-homogeneity for a number of forecasts from the Survey of Professional Forecasters. Therefore, for these variables unbiasedness can only be tested at the individual level.Rational Expectations, Micro-homogeneity, Heterogeneity Bias, Aggregation Bias, Survey Forecasts

Testing the Rational Expectations Hypothesis using Survey Data

Because of the importance of inflation expectations, Lloyd B. Thomas Jr. (Fall 1999, p. 125-44) reexamines "the evidence on the nature and performance of various measures of expected inflation, with special attention given to the issue of rationality" (p. 126). Thomas tests the unbiasedness hypothesis using the Livingston and Michigan survey forecasts for the 1960 to 1997 time period and is unable to reject the null hypothesis of unbiasedness. Unfortunately, two types of problems due to aggregation plague such tests: private information bias and micro-heterogeneity bias. Therefore, for these survey forecasts, consensus regressions should generally not be used to test rationality; rationality can only be tested at the individual level.

Diversity indices applied in desert grassland communities of Otero Mesa, New Mexico

To describe plant community (alpha) diversity on rangelands, managers are confronted with a variety of commonly used indices. The choice, performance, and interpretations of these indices are often not clear. Biodiversity indices were computed for a variety of plant communities in a desert grassland of southern New Mexico. Data consisted of reported importance values, range transect data for both grazed and ungrazed pastures, and search-and-find data specifically addressed to plant community diversity. Occurrence of threatened and endangered plants was considered by a weighting procedure. Performance of each diversity index was evaluated by ranking plant communities from low to high and comparing the rankings yielded by the various indices. Data based upon importance or dominance that omit plant species of lesser importance or dominance should not be the basis of comparisons for alpha diversity. Communities described by range transect data ranked differently depending upon the index used. The most practical measure of plant species diversity may be the number of species found by search-and-find procedures

Obesity prevention in the early years: a mapping study of national policies in England from a behavioural science perspective

Background Evidence indicates that early life is critical for determining future obesity risk. A sharper policy focus on pregnancy and early childhood could help improve obesity prevention efforts. This study aimed to systematically identify and categorise policy levers used in England with potential to influence early lifecourse (pregnancy, 0-5 years) and identify how these interface with energy balance behaviours. The objective is to identify gaps and where further policy actions could most effectively focus. Methods A behavioural science approach was taken using the Capability-Opportunity-Motivation-Behaviour (COM-B) model and Behaviour Change Wheel (BCW) framework. The key determinants of energy balance in the early years were identified from the Foresight Systems Map. Policy actions were scoped systematically from available literature, including any health or non-health policies which could impact on energy balance behaviours. Foresight variables and policy actions were considered in terms of COM-B and the BCW to determine approaches likely to be effective for obesity prevention and treatment. Existing policies were overlaid across the map of key risk factors to identify gaps in obesity prevention and treatment provision. Results A wide range of policy actions were identified (n=115) to address obesity-relevant risk factors. These were most commonly educational or guidelines relating to environmental restructuring (i.e. changing the physical or social context). Scope for strengthening policies relating to the food system (e.g. the market price of food) and psychological factors contributing to obesity were identified. Policies acted via all aspects of the COM-B model, but there was scope for improving policies to increase capability through skills acquisition and both reflective and automatic motivation. Conclusions There is substantial policy activity to address early years obesity but much is focused on education. Scope exists to strengthen actions relating to upstream policies which act on food systems and those targeting psychological factors contributing to obesity risk

Assessment of Pathogens in Flood Waters in Coastal Rural Regions: Case study after Hurricane Michael and Florence

The severity of hurricanes, and thus the associated impacts, is changing over time. One of the understudied threats from damage caused by hurricanes is the potential for cross-contamination of water bodies with pathogens in coastal agricultural regions. Using microbiological data collected after hurricanes Florence and Michael, this study shows a dichotomy in the presence of pathogens in coastal North Carolina and Florida. Salmonella typhimurium was abundant in water samples collected in the regions dominated by swine farms. A drastic decrease in Enterococcus spp. in Carolinas is indicative of pathogen removal with flooding waters. Except for the abundance presence of Salmonella typhimurium, no significant changes in pathogens were observed after Hurricane Michael in the Florida panhandle. We argue that a comprehensive assessment of pathogens must be included in decision-making activities in the immediate aftermath of hurricanes to build resilience against risks of pathogenic exposure in rural agricultural and human populations in vulnerable locations