Inference and Non-Random Sampling

计量经济学分析中的一个常用策略是将感兴趣的变量分为两类。第一类是因变量，其在总体中的分布要用第二类变量，即自变量或解释变量来解释。如果要分析的数据是从感兴趣的总体中随机选择的，那么在给定解释变量的情况下，存在估计因变量条件分布参数的方法。这些估计器是一致的、有效的，并具有其他理想的特性。但是，如果数据不是随机的（应用经济学中经常出现这种情况），一些最重要的属性就不成立了。本项目为经济问题中经常遇到的非随机或部分随机抽样程序开发了既直观又在计算上可行的估计程序和规范测试。这些技术用于分析荷兰劳动力市场参与的部分随机数据集。这个项目解决了如果研究者有关于参数的信息而不是随机数据集的推断问题。本项目研究的抽样方案包括：1)内生分层的样本；2)来自聚合源的因变量分布矩的附加信息；3)包含不同变量集的数据集。这些技术在实际问题中的应用表明它们是可行的，并且可以带来重大的效率收益。

A common strategy in econometric analyses is to divide the variables of interest into two types. The first type is that of a dependent variables whose distribution in the population is to be explained in terms of variables of the second type, the independent or explanatory variables. If the data to be analyzed were randomly selected from the population of interest, then methods exist for estimating the parameters of the conditional distribution of the dependent variable given the explanatory variable. These estimators are consistent, efficient and have other desirable properties. But some of the most important properties do not hold if, as is frequently the case in applied economics, the data are not random. This project develops estimation procedures and specification tests that are both intuitive and computationally feasible for nonrandom or partially random sampling procedures that are frequently encountered in economic problems. These techniques are used to analyze partially random data sets on Dutch labor market participation. This project addresses the question of inference if the researcher has information on the parameters other than a random dataset. Examples of sampling schemes studied by this project include: 1) samples that are endogenously stratified; 2) additional information on moments of the distribution of the dependent variables from aggregate sources; and 3) datasets containing different sets of variables. The application of these techniques to actual problems is used to show that they are feasible and that they can lead to major efficient gains.