Inference and Non-Random Sampling

计量经济学分析中的一个常见策略是将 变量分为两种类型。 第一种是 一个因变量，其在总体中的分布是 可以用第二种类型的变量来解释， 独立或解释变量。 如果数据是 从感兴趣的群体中随机选择被分析的人， 则存在用于估计 因变量的条件分布 解释变量 这些估计是一致的，有效的， 并具有其它所需的性质。 但一些最 重要的属性不成立，如果，通常是这样的情况下， 应用经济学，数据不是随机的。 这个项目 制定评估程序和规格测试， 对于非随机或 部分随机抽样程序， 遇到经济问题。 这些技术用于 分析荷兰劳动力市场的部分随机数据集 参与的 这个项目解决了推理的问题，如果 研究人员有关于参数的信息，而不是随机的 数据集。 本项目研究的抽样方案示例 包括：1）内生分层样本; 2） 关于分布的时刻的附加信息 因变量来自汇总源; 3）数据集 包含不同的变量集。 应用该等 实际问题的技术是用来表明他们是 这是可行的，并能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.