Identification Problems in the Social Sciences

计量经济学家早就发现，将推理问题分成统计和识别两部分是很有用的。鉴定研究试图确定如果研究人员有一个无限大小的样本可以得出的结论的特征。统计推断的研究试图描述一般较弱的结论，这些结论可以在给定的正但有限的样本中得出。统计和鉴定问题以不同的方式限制了实证研究可能得出的结论。当可用的样本很少时，统计问题是最严重的。当研究人员对所研究的群体知之甚少，抽样过程只产生关于群体的微弱数据时，识别问题是最严重的。这项研究继续了研究者对社会科学中识别问题的研究计划。当结果和/或回归量被删减时，尽管有大量关于结果被删减时的回归分析的文献，但令人惊讶的是，很少有人关注当回归量被删减时出现的推理问题。本项目从研究者先前用于分析结果审查和其他非识别抽样过程的相同非参数角度检查回归审查。研究结果用于描述项目和访谈无反应的调查研究可能产生的影响。治疗效果分析的标准形式化假设感兴趣的群体中的每个成员都接受一组相互排斥和详尽的治疗中的一种，并且在实现的治疗下的结果是可观察到的。未实现治疗的结果必然是不可观察的；因此，这些结果是经过审查的。在早期的工作中，研究者研究了经典病例中治疗效果的非参数识别，其中有两种治疗方法，目的是比较这些治疗方法下的结果分布。这个项目检查治疗效果的可识别性，当治疗是有序的，响应函数是已知的单调或凹单调。研究结果可应用于市场需求和生产的计量经济分析。法官如何判决已定罪的未成年犯，一直是政策制定者和犯罪学家关注的问题。研究者正在使用非参数方法来比较两种可供法官选择的主要量刑方案对累犯的影响：监禁在住宅治疗设施和转移到非住宅治疗设施。本项目旨在通过严肃的实证案例研究阐明治疗效果的非参数分析的基本思想，增加新的方法发现，并为犯罪学领域做出实质性贡献。主观预期的本质是研究人类行为的研究人员共同关心的问题。研究者最近设计并实施了经济预期调查（SEE）。SEE是一项正在进行的全国性调查，旨在了解美国人对近期经济前景的看法。概率问题被用来引出人们对未来一年的收入、就业、医疗保险覆盖面和犯罪受害情况的预期。最近完成的SEE数据分析检查了受访者的收入预期和他们对经济不安全的看法。该项目将继续以目前每年约1500次访谈的速度收集阿拉善阿拉善机构的数据，并将进行一套特别研究，以提高调查的价值。

Econometricians have long found it useful to separate inferential problems into statistical and identification components. Studies of identification seek to characterize the conclusions that could be drawn if the researcher had available a sample of unlimited size. Studies of statistical inference seek to characterize the generally weaker conclusions that can be drawn given a sample of positive but finite size. Statistical and identification problems limit in distinct ways the conclusions that may be drawn in empirical research. Statistical problems are most severe when the available sample is small. Identification problems are most severe when the researcher knows little about the population under study and the sampling process yields only weak data on the population. This research continues the investigator's program of work on identification problems in the social sciences. Four projects combining methodological research and empirical analysis are planned: Nonparametric Regression When Outcomes and/or Regressors are Censored Whereas there is a voluminous literature on regression analysis when outcomes are censored, surprisingly little attention has been given to the inferential problems that arise when regressors are censored. This project examines regressor censoring from the same nonparametric perspective used earlier by the investigator to analyze outcome censoring and other non identifying sampling processes. The findings are used to characterize the possible implications for survey research of item and interview nonresponse. Monotone Treatment Response The standard formalization of the analysis of treatment effects assumes that each member of a population of interest receives one of a set of mutually exclusive and exhaustive treatments, and that the outcome under the realized treatment is observable. Outcomes under the non realized treatments are necessarily unobservable; hence these outcomes are censored. In earlier work, the investigator has studied the nonparametric identification of treatment effects in the classical case where there are two treatments and the objective is to compare distributions of outcomes under these treatments. This project examines the identifiability of treatment effects when treatments are ordered and response functions are known to be monotone or concave monotone. The findings have application to the econometric analysis of market demand and of production. Empirical Analysis of Sentencing and Recidivism The question of how judges should sentence convicted juvenile offenders has long concerned policy makers and criminologists. The investigator is using nonparametric methods to compare the effects on recidivism of the two main sentencing options available to judges: confinement in residential treatment facilities and diversion to nonresidential treatment. This project seeks to illuminate the basic ideas of nonparametric analysis of treatment effects through a serious empirical case study, to add new methodological findings, and to contribute substantively to the field of criminology. Analyses of the Survey of Economic Expectations The nature of subjective expectations is a shared concern of researchers who study human behavior. The investigator has recently designed and implemented the Survey of Economic Expectations (SEE). SEE is an ongoing national survey initiated in an effort to learn how Americans perceive their near term economic prospects. Probabilistic questions are used to elicit expectations about income, employment, health insurance coverage, and crime victimization one year into the future. Recently completed analyses of SEE data examine respondents' income expectations and their perceptions of economic insecurity. This project would continue collection of SEE data at the present rate of about 1500 interviews per year and would perform a set of special studies that should enhance the value of the survey.