反事实与合成控制方法：基于工具变量主成分分析的因果推断
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis
AI总结：
本研究提出一种基于工具变量主成分分析的反事实与合成控制因果推断方法，用预测协变量工具化因子载荷，可处理高维数据、降低模型误设风险与偏差，并用于评估英国脱欧对英外国直接投资的影响。
摘要：
本文在反事实与合成控制的框架下，提出了一种用于因果推断的新方法。与广义合成控制方法相匹配，我们的工具变量主成分分析方法使用具有预测能力的协变量作为因子载荷的工具变量，而非将其纳入回归项。这些经工具变量处理的因子载荷呈现出随时间变化的动态特征，具备更好的经济学解释性。协变量通过变换矩阵$Γ$进行工具化处理，当协变量数量较多时，可依据少量潜在因子轻松实现降维，帮助我们有效处理高维数据集，同时让模型更简洁。此外，这种处理协变量的新方式更不易受模型误设的影响，且能实现更高的预测精度。我们的模拟实验表明，在存在未观测到的协变量的情况下，该方法相比其他主流方法偏差更小。在实证应用中，我们使用所提出的方法评估了英国脱欧对流入英国的外国直接投资的影响。
英文摘要：
In this paper, we propose a novel method for causal inference within the framework of counterfactual and synthetic control. Matching forward the generalized synthetic control method, our instrumented principal component analysis method instruments factor loadings with predictive covariates rather than including them as regressors. These instrumented factor loadings exhibit time-varying dynamics, offering a better economic interpretation. Covariates are instrumented through a transformation matrix, $Γ$, when we have a large number of covariates it can be easily reduced in accordance with a small number of latent factors helping us to effectively handle high-dimensional datasets and making the model parsimonious. Moreover, the novel way of handling covariates is less exposed to model misspecification and achieved better prediction accuracy. Our simulations show that this method is less biased in the presence of unobserved covariates compared to other mainstream approaches. In the empirical application, we use the proposed method to evaluate the effect of Brexit on foreign direct investment to the UK.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
