Notes on testing carry-over effects in continuous data under an incomplete block crossover design

We consider hypothesis testing and estimation of carry-over effects in continuous data under an incomplete block crossover design when comparing two experimental treatments with a placebo. We develop procedures for testing differential carry-over effects based on the weighted-least-squares (WLS) method. We apply Monte Carlo simulations to evaluate the performance of these test procedures in a variety of situations. We use the data regarding the forced expiratory volume in one second (FEV₁) readings taken from a double-blind crossover trial comparing two different doses of formoterol with a placebo to illustrate the use of test procedures proposed here.     

Treatment Evaluation in the Presence of Sample Selection

Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.     

我国商业信用的资源再配置效应与强制性特征——基于工业企业数据的实证检验

商业信用的资源再配置假说认为,商业信用可实现银行信贷资金的再配置,从而提高资源配置效率。以2004—2007年中国工业企业微观数据为基础,对我国商业信用的资源配置效应以及恶意拖欠基础上的强制性信用特征进行的检验结果表明,在我国,获取更多银行信贷的企业(包括国有企业)不仅未能提供更多商业信用,反而获取了更多商业信用,而市场势力的存在可能是导致这一配置结果的扭曲性因素;拥有更多应收账款的企业倾向于扩大应付账款规模,从而使企业间债务关系呈现出较强的"三角债"趋势。此外,与出口企业相比,产品内销企业在商业信用的供给中面临着更多的坏账损失风险。这说明我国商业信用的产生具有显著的恶意拖欠特征与违约风险。我国的商业信用不仅未能改善资源配置效率,反而增大了市场的交易费用和运行风险。     

Where do we stand? Recent AEA member views on professionalization

The professionalization of evaluation continues to be debated at numerous conferences in the U.S. and abroad. At this time, AEA member views on the potential benefits and negative side effects of professionalization are essential as the discussion evolves. This study provides recent views on major topics in professionalization, including potential benefits, negative side effects, processes, competencies, and procedures. Results from in-depth interviews and an online survey demonstrate that AEA members view potential benefits of professionalization to be stakeholder trust, evaluator reputation and identity, while concerns about a potential negative side effect known as the “narrowing effect” (i.e., some evaluators will be alienated based on their background, competencies, etc.) were expressed by participants. These recent findings can inform the ongoing discussion of professionalization, and suggest new directions for future research on evaluation.     

Response of sensitive behaviors to frequent measurement

We study the influence of frequent survey measurement on behavior. Widespread access to the Internet has made important breakthroughs in frequent measurement possible—potentially revolutionizing social science measurement of processes that change quickly over time. One key concern about using such frequent measurement is that it may influence the behavior being studied. We investigate this possibility using both a population-based experiment with random assignment to participation in a weekly journal for twelve months (versus no journal) and a large-scale, population-based, journal-keeping study with weekly measurement for 30 months. Results reveal few of the measured behaviors are correlated with assignment to frequent measurement. Theoretical reasoning regarding the likely behavioral response to frequent measurement correctly predicts domains most vulnerable to this possibility. Overall, however, we found little evidence of behavioral response to frequent measurement.     

基于动态因子Copula模型的行业间系统性风险分析

国际金融市场间的相关关系以及系统性风险受到很多学者的重视，本文则以我国股市的行业指数作为研究对象进行实证研究。通过构建动态因子Copula模型，文章对行业的日收益率数据进行了动态相关性分析，并基于风险预期占比度量了我国行业之间系统性风险的溢出效应。本文分析了2006年1月4日至2016年7月1日的28个行业指数数据，基于GAS动态负荷因子的变化路径来刻画其相关关系，通过风险预期占比来研究行业间的风险溢出效应。研究表明，各个行业指数收益率之间存在较强的关联性。就单个行业来说，化工行业与其他行业关系最为不稳定。就金融与非金融行业而言，金融行业对非金融行业的影响较大且较为平稳。本文所得研究结果可以为投资者和风险管理者在进行决策时提供一定的指导。     

Bounded risk estimation of a linear combination of location parameters in negative exponential distributions via three-stage sampling

The paper deals with the problem of bounded risk point estimation for a linear combination of location parameters of two negative exponential distributions. Isogai and Futschik considered the situation when the location and scale parameters are all unknown. They proposed purely sequential procedures and gave second order expansions of the average sample sizes and risks. In this paper we propose three-stage procedures and derive second order expansions of the average sample sizes and risks. Further, we compare the results with those from previous work.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.     

Can the welfare state replace parents? Children's cognition in the United States and Great Britain

We compare family and parental effects on child verbal facility, verbal achievement and mathematics achievement in the United States and Great Britain. We study 3,438 5–13 year-old children from the 1994 NLSY Child-Mother Data Set and 1429 same-aged children from the National Child Development Study, also known as the British Child. Multivariate analyses suggest that the processes through which families invest in child cognition are similar across societies, with factors including low birth weight, child health, maternal cognition, family size and children's home environments being consequential. We conclude that parental investments are equally important across the two societies. The more developed welfare state in Great Britain does not notably compensate for parental investments in that society, although it may play a greater role when parental resources are absent or stretched thin.