论文标题
相同的根不同的叶子：时间序列和面板数据中的横截面方法
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data
论文作者
论文摘要
社会科学的一个核心目标是评估政策的因果关系。一种主要的方法是通过面板数据分析，其中观察到多个单元的行为。跨时间和空间的信息激发了两种一般的方法：（i）水平回归（即不满意的），它利用了时间序列模式，以及（ii）垂直回归（例如，合成控制），利用了横截面模式。传统观念指出，两种方法在根本上是不同的。我们确定该立场是部分错误以进行估计，但通常对推论为真实。特别是，我们证明两种方法都在几种标准设置下产生相同的点估计值。但是，对于相同的点估计，每种方法都量化了相对于明显的估计的不确定性。反过来，一个估计的置信区间可能对另一个估计的覆盖率不正确。这强调了研究人员假设的随机性来源对推理的准确性有直接影响。
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are observed over time. The information across time and space motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, and (ii) vertical regression (e.g., synthetic controls), which exploits cross-sectional patterns. Conventional wisdom states that the two approaches are fundamentally different. We establish this position to be partly false for estimation but generally true for inference. In particular, we prove that both approaches yield identical point estimates under several standard settings. For the same point estimate, however, each approach quantifies uncertainty with respect to a distinct estimand. In turn, the confidence interval developed for one estimand may have incorrect coverage for another. This emphasizes that the source of randomness that researchers assume has direct implications for the accuracy of inference.
