标题： 偶尔误指定 显示英文标题

标题： Occasionally Misspecified

摘要： 在对某一经济模型拟合样本数据时，该模型对于某些观测值可能是严重设定错误的。 这可能是因为未建模的独特事件，例如政策的突然但短暂的变化。 这些异常值可以显著改变估计和推断。 需要一个稳健的估计来限制它们的影响。 对于偏斜的数据，这还会引起另一种偏差，也可能使估计和推断无效。 本文提出了一种具有简单偏差校正的稳健GMM估计器，该偏差校正不会显著降低稳健性。 文章提供了有限样本下的稳健性边界，并且与剔除所有异常值的oracle在渐近条件下具有均匀等价性。 由此得出一致性及渐近正态性。 应用于“价格谜团”的案例研究，即发现当货币政策收紧时通货膨胀增加，展示了这些担忧和方法。 所提出的估计器发现了直观的结果：更紧缩的货币政策导致通货膨胀下降。 显示英文摘要

摘要： When fitting a particular Economic model on a sample of data, the model may turn out to be heavily misspecified for some observations. This can happen because of unmodelled idiosyncratic events, such as an abrupt but short-lived change in policy. These outliers can significantly alter estimates and inferences. A robust estimation is desirable to limit their influence. For skewed data, this induces another bias which can also invalidate the estimation and inferences. This paper proposes a robust GMM estimator with a simple bias correction that does not degrade robustness significantly. The paper provides finite-sample robustness bounds, and asymptotic uniform equivalence with an oracle that discards all outliers. Consistency and asymptotic normality ensue from that result. An application to the "Price-Puzzle," which finds inflation increases when monetary policy tightens, illustrates the concerns and the method. The proposed estimator finds the intuitive result: tighter monetary policy leads to a decline in inflation.