Estimation of Cross-sectional and Panel Data Duration Models with General Forms of Censoring (Revised)

Prop ID：SES-0452364P I：Khan，Shakeeb组织：罗切斯特大学标题：横截面和面板数据持续期模型的估计和审查拟议活动的主要智力优点的一般形式是开发新的计量经济学和统计学方法，用于在持续期模型中进行估计和推断。本提案中考虑的统计模型的动机是持续期数据的特殊特征，这些特征通常出现在经验环境中。例如，审查经常出现在事件发生前的数据中，原因多种多样，通常是经验研究人员观察或数据收集计划的结果。失业持续时间可能会被审查，因为代理人从样本中消失，或者为了控制数据收集成本，失业代理人只被跟踪很短的一段时间。如果他们在这段时间结束时仍然失业，他们的法术长度将被审查。在这项建议中引入的估计量将允许一种一般形式的审查，在某种意义上，它们允许审查变量以任意方式依赖于解释持续时间长度的观察变量。这是由(但不限于)特定的经验背景所驱动的。对于某些福利计划，如TANF(对贫困家庭的临时援助)，福利取决于个人劳动力市场历史。在这种情况下，直到找到工作的持续时间将在权利到期时进行审查。除了一般形式的审查外，该提案还包括对在实践中遇到久期数据的其他特征的模型进行新的估计程序。其中一类持续时间模型是用于面板数据的模型，在该模型中，研究人员观察同一个体的多次事件，或同一组中的不同个体，即家庭成员或同一行业内的公司。一般来说，面板数据允许研究人员控制横截面数据集中无法控制的未观察到的因素。在持续时间模型的背景下，这项建议为面板数据模型提出了一个新的估计器，允许进行非常一般的形式(在这种情况下是特定于拼写的)审查。该建议的其他组成部分开发了久期模型的估计器，这些模型表现出以下特征：双重删失数据、具有时变协变量的久期模型和股票采样的久期数据，所有这些都在实践中遇到过。广泛的影响：该项目为应用经济学家提供了在久期分析中进行推断的新工具。久期模型被广泛应用于应用经济学的各个领域，因为它们被用来研究重要的政策问题，如福利改革的影响、税收激励对退休的影响、失业保险的激励效应等。持续期模型也被用于劳动、发展、公共财政和金融。这里介绍的新方法和软件代码将在调查员的网站上提供。

ABSTRACTProp ID: SES-0452364P I: Khan, Shakeeb Organization: University of Rochester Title: Estimation of Cross-sectional and Panel Data Duration Models with General Forms of Censoring The primary intellectual merit of the proposed activities is the development of new econometric and statistical methods for conducting estimation and inference in duration models. The statistical models considered in this proposal are motivated by particular features of duration data, which often arise in empirical settings. For example, censoring often arises in time-to event data, for a variety of reasons that are usually a consequence of the empirical researcher's observation or data collection plan. Unemployment spell-length may be censored because the agent is lost from the sample, or to control data collection costs, unemployed agents are only followed for short period of time. If they are still unemployed at the end of this period, their spell length is censored. The estimators introduced in this proposal will permit a general form of censoring, in the sense that they permit the censoring variable to depend in an arbitrary way on the observed variables that explain the duration length. This is motivated by, (but not restricted to) a specific empirical setting. With certain welfare programs, such as those in TANF (Temporary Assistance to Needy Families), entitlement depends on individual labor market histories. In this case, duration until one finds employment would be censored at the period when entitlement expires. In addition to general forms of censoring, the proposal includes new estimation procedures for models encountering other features of duration data encountered in practice. One such class of duration models is those for panel data, where the researcher observes multiple times-to-event, either for the same individual, or for different individuals within the same group, i.e. either family members, or firms within the same industry. Generally speaking, panel data allows researchers to control for unobserved factors that cannot be controlled for in a cross-sectional data set. In the context of durations models, this proposal suggests a new estimator for a panel data model which allows for a very general form (in this case spell specific) censoring. The other components of this proposal develop estimators for duration models which exhibit the following features: doubly censored data, duration models with time varying covariates, and stock sampled duration data, all of which have been encountered in practice.Broader Impacts: The project provides applied economists with new tools to conduct inference in duration analysis. Duration models are widely applied in practice in a variety of fields in applied economics, since they are used to address important policy issues, such as the effect of welfare reform, tax incentive effects on retirement, and the incentive effects of unemployment insurance. Duration models are also used in labor, development, public finance and finance. The new methods introduced here, and the software code will be made available on the investigator's website.