观测时间序列中动态因果效应的半参数推断
Semiparametric Inference for Dynamic Causal Effects from Observational Time Series
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对观测时间序列中动态因果效应推断的挑战，提出结合去偏机器学习与工具变量的半参数框架，并验证其在货币政策应用中的有效性。
AI中文摘要：
在观测时间序列中，针对一次性干预在多个时域上的动态因果效应的统计推断，因高维观测到的预处理信息、未测量的混杂因素以及序列依赖性而变得复杂。为应对这些挑战，我们开发了一个半参数框架，用于从单一序列相关的时间序列中进行推断，该框架通过缓冲块交叉拟合将去偏机器学习与工具变量相结合。在几何β混合条件下，我们推导了估计误差的非渐近界、每个固定时域上的渐近正态性，以及适应序列依赖性的可行推断。我们进一步展示了如何利用时间依赖下的学习者特定预测保证来验证正交推断所需的干扰率条件。在一项包含468个月和1464个滞后FRED-MD控制变量的货币政策应用中，我们表明工具化的政策紧缩在中期时域降低了新屋开工数，敏感性分析支持了这一发现。
英文摘要：
In observational time series, statistical inference for dynamic causal effects of a one-time intervention across horizons is complicated by high-dimensional observed pre-treatment information, unmeasured confounding, and serial dependence. To address these challenges, we develop a semiparametric framework for inference from a single serially dependent time series, integrating debiased machine learning with instrumental variables through buffered block cross-fitting. Under geometric beta-mixing, we derive non-asymptotic bounds on estimation error, asymptotic normality at each fixed horizon, and feasible inference that accommodates serial dependence. We further show how learner-specific prediction guarantees under temporal dependence can be used to verify the nuisance-rate conditions required for orthogonal inference. In a monetary-policy application with 468 months and 1464 lagged FRED-MD controls, we show an instrumented policy tightening lowers housing starts at medium horizons, with sensitivity analyses that support the finding.
