论文标题
用单个处理的集群推断
Inference with a single treated cluster
论文作者
论文摘要
我介绍了一种通用方法，用于推断研究设计中的标量参数，其中只有一个群集接受治疗，具有有限数量的异质簇。这种情况在差异差异估计中很普遍，但此处开发的测试更普遍。我表明，测试控制大小并具有在渐近学下具有功率，其中每个群集中的观测值较大，但簇数是固定的。该测试结合了加权，大约高斯参数估计与重排过程，以获得其临界值。大多数经验相关情况所需的权重在论文中列出。临界值的计算在计算上很简单，不需要模拟或重采样。重排测试对于某些集群比其他簇要变化得多的情况非常强大。提供了示例和经验应用。
I introduce a generic method for inference about a scalar parameter in research designs with a finite number of heterogeneous clusters where only a single cluster received treatment. This situation is commonplace in difference-in-differences estimation but the test developed here applies more generally. I show that the test controls size and has power under asymptotics where the number of observations within each cluster is large but the number of clusters is fixed. The test combines weighted, approximately Gaussian parameter estimates with a rearrangement procedure to obtain its critical values. The weights needed for most empirically relevant situations are tabulated in the paper. Calculation of the critical values is computationally simple and does not require simulation or resampling. The rearrangement test is highly robust to situations where some clusters are much more variable than others. Examples and an empirical application are provided.
