A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression*
提出一种Bootstrap方法，用于在实际数据中评估图论因果搜索算法识别的结构向量自回归因果顺序的可靠性，并通过实例和模拟验证其有效性。
Abstract Graph‐theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes et al . (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data‐based contemporaneous causal order for the structural vector autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data‐generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph‐theoretic search algorithms.