New species of Bakeriella (Hymenoptera, Bethylidae) from the New World and new data on geographic distribution

Bakeriella lata sp. nov. (Brazil, Rondônia), Bakeriella aurata sp. nov. (Brazil, Amazonas) and Bakeriella sulcaticeps sp. nov. (Brazil, Amazonas) are described and illustrated. New geographic records and variation data for B. cristata Evans, 1964, B. floridana Evans, 1964, B. flavicornis Kieffer, 1910, B. incompleta Azevedo, 1994, B. mira Evans, 1997, B. montivaga (Kieffer, 1910), B. olmeca Evans, 1964 and B. subcarinata Evans, 1965 are provided. The male of B. incompleta is described for the first time.

Predictability of drug expenditures: An application using morbidity data

The growth of pharmaceutical expenditure and its prediction is a major concern for policy makers and health care managers. This paper explores different predictive models to estimate future drug expenses, using demographic and morbidity individual information from an integrated healthcare delivery organization in Catalonia for years 2002 and 2003. The morbidity information consists of codified health encounters grouped through the Clinical Risk Groups (CRGs). We estimate pharmaceutical costs using several model specifications, and CRGs as risk adjusters, providing an alternative way of obtaining high predictive power comparable to other estimations of drug expenditures in the literature. These results have clear implications for the use of risk adjustment and CRGs in setting the premiums for pharmaceutical benefits.

Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approachlinks the observables to the model counterparts via a flexible specification which doesnot require the model-based component to be solely located at business cycle frequencies,allows the non model-based component to take various time series patterns, andpermits model misspecification. Applying standard data transformations induce biasesin structural estimates and distortions in the policy conclusions. The proposed approachrecovers important model-based features in selected experimental designs. Twowidely discussed issues are used to illustrate its practical use.

Crash Data Validation: An Iowa Case Study, February, 2007

With the quickening pace of crash reporting, the statistical editing of data on a weekly basis, and the ability to provide working databases to users at CTRE/Iowa Traffic Safety Data Service, the University of Iowa, and the Iowa DOT, databases that would be considered incomplete by past standards of static data files are in “public use” even as the dynamic nature of the central DOT database allows changes to be made to both the aggregate of data and to the individual crashes already reported. Moreover, “definitive” analyses of serious crashes will, by their nature, lag seriously behind the preliminary data files. Even after these analyses, the dynamic nature of the mainframe data file means that crash numbers can continue to change long after the incident year. The Iowa DOT, its Office of Driver Services (the “data owner”), and institutional data users/distributors must establish data use, distribution, and labeling protocols to deal with the new, dynamic nature of data. In order to set these protocols, data must be collected concerning the magnitude of difference between database records and crash narratives and diagrams. This study determines the difference between database records and crash narratives for the Iowa Department of Transportation’s Office of Traffic and Safety crash database and the impacts of this difference.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Status of Young Women in the Juvenile Justice System Preliminary Data, 2007

Young women involved in the juvenile justice system present with characteristics and experiences that differentiate them from their male counterparts. As such, the juvenile justice system in Iowa must consider these factors if it is to effectively and efficiently impact recidivism and rehabilitation.

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for U.S. output growth and inflationusing a number of commonly used forecasting models that rely on a large number ofmacroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizationsout-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have theopposite effect on higher moments. We find that normality is rejected for most modelsin some dimension according to at least one of the tests we use. Interestingly, however,combinations of predictive densities appear to be correctly approximated by a normaldensity: the simple, equal average when predicting output growth and Bayesian modelaverage when predicting inflation.

Does direct democracy reduce the size of government? New evidence from historical data, 1890-2000

Using historical data for all Swiss cantons from 1890 to 2000, we estimate the causal effect of direct democracy on government spending. The main innovation in this paper is that we use fixed effects to control for unobserved heterogeneity and instrumental variables to address the potential endogeneity of institutions. We find that the budget referendum and lower costs to launch a voter initiative are effective tools in reducing canton level spending. However, we find no evidence that the budget referendum results in more decentralized government or a larger local government. Our instrumental variable estimates suggest that a mandatory budget referendum reduces the size of canton spending between 13 and 19 percent. A 1 percent lower signature requirement for the initiative reduces canton spending by up to 2 percent.