High precision implementation of Steck's recursion method for use in goodness-of-fit tests

Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck''s recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck''s method are discussed and compared with solutions provided in high precision libraries.     

DYNAMIC TREATMENT EFFECTS OF TEACHER'S AIDES IN AN EXPERIMENT WITH MULTIPLE RANDOMIZATIONS

Using data from a large‐scale two‐stage experiment wherein students and teachers were randomized at both kindergarten entry and first grade to be sorted into classes either with or without full‐time teacher's aides, I estimate an econometric model that is uniquely suited to take advantage of this design to determine their effect on academic achievement. The identification strategy produces fully nonparametric dynamic average treatment effects for every treatment path. I find that the use of full‐time teacher's aides increases student achievement, but the benefits appear to accrue mostly to those of higher socioeconomic status and to White students. A cost‐benefit analysis shows that full‐time teacher's aides may be a competitive alternative to class size reductions in terms of net social benefits. (JEL I21, I28)     

“Know What to Do If You Encounter a Flash Flood”: Mental Models Analysis for Improving Flash Flood Risk Communication and Public Decision Making

Understanding how people view flash flood risks can help improve risk communication, ultimately improving outcomes. This article analyzes data from 26 mental models interviews about flash floods with members of the public in Boulder, Colorado, to understand their perspectives on flash flood risks and mitigation. The analysis includes a comparison between public and professional perspectives by referencing a companion mental models study of Boulder‐area professionals. A mental models approach can help to diagnose what people already know about flash flood risks and responses, as well as any critical gaps in their knowledge that might be addressed through improved risk communication. A few public interviewees mentioned most of the key concepts discussed by professionals as important for flash flood warning decision making. However, most interviewees exhibited some incomplete understandings and misconceptions about aspects of flash flood development and exposure, effects, or mitigation that may lead to ineffective warning decisions when a flash flood threatens. These include important misunderstandings about the rapid evolution of flash floods, the speed of water in flash floods, the locations and times that pose the greatest flash flood risk in Boulder, the value of situational awareness and environmental cues, and the most appropriate responses when a flash flood threatens. The findings point to recommendations for ways to improve risk communication, over the long term and when an event threatens, to help people quickly recognize and understand threats, obtain needed information, and make informed decisions in complex, rapidly evolving extreme weather events such as flash floods.     

THE IMPACT OF SCHOOL RACIAL COMPOSITIONS ON NEIGHBORHOOD RACIAL COMPOSITIONS: EVIDENCE FROM SCHOOL REDISTRICTING

I use data surrounding public school redistricting to study how school racial compositions affect neighborhood racial compositions. This redistricting followed from the end of court‐ordered busing for racial desegregation, significantly changing the racial composition of the assigned school for many neighborhoods. Over a 5‐year period, the impact of an increase in the percent black of the assigned elementary school on the percent black of the neighborhood was positive. The effects increased over time, consistent with a simple model of short‐run neighborhood racial dynamics. These results have implications for potential effects of school racial desegregation policy changes on neighborhood racial compositions. (JEL H75, I28, R23)     

The Pacific Rim Library: A Surprising Pearl

The Pacific Rim Library (PRL) is an initiative of the Pacific Rim Digital Library Association (PRDLA). The project began in 2006 using the OAI-PMH paradigm and now holds over 300,000 records harvested from OAI data provider libraries around the Pacific. PRL's goal is to enable the sharing of digital collections amongst PRDLA members and the world, but greater unexpected benefits have been discovered. Through mirroring their metadata, PRL increases the chance that their data will be discovered in Google and other general search engines. With its many disparate collections, PRL is not a repository for traditional information discovery and retrieval. Initially users will bounce from a Google hit to the PRL metadata record in Hong Kong and then begin an intensive search on the original site which hosts the full digital object, in Vancouver, Honolulu, Wuhan, Singapore, or other PRDLA member location.     

Amplification of Sensitivity Analysis in Matched Observational Studies

A sensitivity analysis displays the increase in uncertainty that attends an inference when a key assumption is relaxed. In matched observational studies of treatment effects, a key assumption in some analyses is that subjects matched for observed covariates are comparable, and this assumption is relaxed by positing a relevant covariate that was not observed and not controlled by matching. What properties would such an unobserved covariate need to have to materially alter the inference about treatment effects? For ease of calculation and reporting, it is convenient that the sensitivity analysis be of low dimension, perhaps indexed by a scalar sensitivity parameter, but for interpretation in specific contexts, a higher dimensional analysis may be of greater relevance. An amplification of a sensitivity analysis is defined as a map from each point in a low dimensional sensitivity analysis to a set of points, perhaps a 'curve,' in a higher dimensional sensitivity analysis such that the possible inferences are the same for all points in the set. Possessing an amplification, an investigator may calculate and report the low dimensional analysis, yet have available the interpretations of the higher dimensional analysis.