The Adjustment of Prices and the Adjustment of the Exchange Rate

The purchasing power parity puzzle relates to the adjustment of real exchange rates. Real exchange rates are extremely volatile, suggesting that temporary shocks emanate from the monetary sector. But the half-life of real exchange rate deviations is extremely large -- 2.5 to 5 years. This half-life seems too large to be explained by the slow adjustment of nominal prices. We offer a different interpretation. We maintain that nominal exchange rates and prices need not converge at the same rate, as is implicit in rational-expectations sticky-price models of the exchange rate. Evidence from an unobserved components model for nominal prices and nominal exchange rates that imposes relative purchasing power parity in the long run indicates that nominal exchange rates converge much more slowly than nominal prices. The real puzzle is why nominal exchange rates converge so slowly.

Exchange Rate Models Are Not as Bad as You Think

Standard models of exchange rates, based on macroeconomic variables such as prices, interest rates, output, etc., are thought by many researchers to have failed empirically. We present evidence to the contrary. First, we emphasize the point that "beating a random walk" in forecasting is too strong a criterion for accepting an exchange rate model. Typically models should have low forecasting power of this type. We then propose a number of alternative ways to evaluate models. We examine in-sample fit, but emphasize the importance of the monetary policy rule, and its effects on expectations, in determining exchange rates. Next we present evidence that exchange rates incorporate news about future macroeconomic fundamentals, as the models imply. We demonstrate that the models might well be able to account for observed exchange-rate volatility. We discuss studies that examine the response of exchange rates to announcements of economic data. Then we present estimates of exchange-rate models in which expected present values of fundamentals are calculated from survey forecasts. Finally, we show that out-of-sample forecasting power of models can be increased by focusing on panel estimation and long-horizon forecasts.

Reducing Selection Bias in Analyzing Longitudinal Health Data with High Mortality Rates

Two longitudinal regression models, one parametric and one nonparametric, are developed to reduce selection bias when analyzing longitudinal health data with high mortality rates. The parametric mixed model is a two-step linear regression approach, whereas the nonparametric mixed-effects regression model uses a retransformation method to handle random errors across time

County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control

Abstract Background Epidemiological analyses of aggregated data are often used to evaluate theoretical health effects of natural disasters. Such analyses are susceptible to confounding by unmeasured differences between the exposed and unexposed populations. To demonstrate the difference-in-difference method our population included all recorded Florida live births that reached 20 weeks gestation and conceived after the first hurricane of 2004 or in 2003 (when no hurricanes made landfall). Hurricane exposure was categorized using ≥74 mile per hour hurricane wind speed as well as a 60 km spatial buffer based on weather data from the National Oceanic and Atmospheric Administration. The effect of exposure was quantified as live birth rate differences and 95 % confidence intervals [RD (95 % CI)]. To illustrate sensitivity of the results, the difference-in-differences estimates were compared to general linear models adjusted for census-level covariates. This analysis demonstrates difference-in-differences as a method to control for time-invariant confounders investigating hurricane exposure on live birth rates. Results Difference-in-differences analysis yielded consistently null associations across exposure metrics and hurricanes for the post hurricane rate difference between exposed and unexposed areas (e.g., Hurricane Ivan for 60 km spatial buffer [−0.02 births/1000 individuals (−0.51, 0.47)]. In contrast, general linear models suggested a positive association between hurricane exposure and birth rate [Hurricane Ivan for 60 km spatial buffer (2.80 births/1000 individuals (1.94, 3.67)] but not all models. Conclusions Ecological studies of associations between environmental exposures and health are susceptible to confounding due to unmeasured population attributes. Here we demonstrate an accessible method of control for time-invariant confounders for future research

High Healthcare Utilization at the Onset of Medically Unexplained Symptoms

Objective: Patients with medically unexplained syndromes (MUS) often do not receive appropriate healthcare. A critical time for effective healthcare is the inception of MUS. The current study examined data from a prospective longitudinal study of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) veterans to understand the relationship of increasing physical symptom burden to healthcare utilization. Methods: Data was examined from a prospective study of OEF/OIF veterans assessed before and one year after deployment (n=335). Physical symptom burden was measured with the Patient Health Questionnaire (PHQ-15). Analyses were conducted with polynomial regression and response surface analysis (RSA). Results: Increases in physical symptom burden predicted greater healthcare utilization one year after deployment: primary care practitioner (slope = -0.26, F=4.07, p=0.04), specialist (slope = -.43, t=8.67, p=0.003), allied health therapy (e.g., physical therapy) (slope = -.41, t=5.71, p=0.02) and mental health (slope = -.32, F=4.04, p=0.05). There were no significant difference in utilization between those with consistently high levels and those with increases in physical symptom burden. Conclusion: This is the first prospective study to examine, and show, a relationship between onset of clinically significant physical symptoms and greater healthcare utilization. Our data suggest that patients with increasing physical symptom burden have the same level of healthcare as patients with chronic physical symptom burden. Needed next steps are to better understand the quality of care at inception and determine how to intervene so that recommended approaches to care are provided from the onset

Coastal Wetland Restoration through the lens of Odum\u27s theory of ecosystem development

Advancing ecological restoration assessments requires a more detailed consideration of species interactions and ecosystem processes. Most restoration projects rely on a few metrics not always directly linked with ecological theory. Here, we used Odum\u27s theory of ecosystem development to assess and compare the ecosystem structure and services of created marshes (4–6 years old) with preexisting, reference marshes in a brackish water region of the Mississippi River Delta. We built ecosystem models for created and reference marshes that integrated large datasets of stomach contents, stable isotopes, and taxa abundances. Despite strong resemblance in community structure, created marshes were at an earlier succession stage compared to the reference marshes, having lower biomass (including exploited species), higher biomass turnover and production, less dependence on detritus, lower material cycling, and less energy flowing through specialist pathways. Although preserving preexisting marshes should be a priority, created marshes may still be an important tool for the restoration of coastal areas and their ecosystem services. In addition, our results show that comparisons of species biodiversity alone may fail to capture essential differences in ecosystem processes between habitats, which reinforces the importance of ecosystem modeling approaches to assess restoration projects

An Isosurface Continuity Algorithm for Super Adaptive Resolution Data

We present the chain-gang algorithm for isosurface rendering of super adaptive resolution (SAR) volume data in order to minimize (1) the space needed for storage of both the data and the isosurface and (2) the time taken for computation. The chain-gan

Meeting report : 1st international functional metagenomics workshop May 7–8, 2012, St. Jacobs, Ontario, Canada

This report summarizes the events of the 1st International Functional Metagenomics Workshop. The workshop was held on May 7 and 8 in St. Jacobs, Ontario, Canada and was focused on building a core international functional metagenomics community, exploring strategic research areas, and identifying opportunities for future collaboration and funding. The workshop was initiated by researchers at the University of Waterloo with support from the Ontario Genomics Institute (OGI), Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Waterloo