Inference after estimation of breaks
Inference after estimation of breaks
复制标题
估计断点后的推断
DOI：
10.1016/j.jeconom.2020.07.036
复制
发表时间：
2021
影响因子：
6.3
通讯作者：
McCloskey, Adam
中科院分区：
文献类型：
--
作者：
Andrews, Isaiah;Kitagawa, Toru;McCloskey, Adam
在一类重要的计量经济学问题中，研究人员通过最大化数据依赖向量的欧几里德范数来选择目标参数。可以纳入这一框架的例子包括具有估计阈值的阈值回归模型和具有估计断裂日期的结构断裂模型。当目标参数的随机性不可忽略时，忽略该随机性的估计和推断程序可能会严重偏倚和误导。本文研究了在这种情况下的条件和无条件推理，占数据依赖的目标参数的选择。详细讨论了具有正确覆盖的分位数无偏估计和置信集的构造，并证明了它们在目标参数在极限内保持随机的数据生成过程下的渐近有效性.我们还提供了一种新的样本分裂方法，改进了传统的分裂样本推理。
In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds and structural break models with estimated break dates. Estimation and inference procedures that ignore the randomness of the target parameter can be severely biased and misleading when this randomness is non-negligible. This paper studies conditional and unconditional inference in such settings, accounting for the data-dependent choice of target parameters. We detail the construction of quantile-unbiased estimators and confidence sets with correct coverage, and prove their asymptotic validity under data generating process such that the target parameter remains random in the limit. We also provide a novel sample splitting approach that improves on conventional split-sample inference.