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A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression *
Author(s) -
Demiralp Selva,
Hoover Kevin D.,
Perez Stephen J.
Publication year - 2008
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2007.00496.x
Subject(s) - causal structure , vector autoregression , monte carlo method , autoregressive model , computer science , econometrics , graph , causal model , process (computing) , mathematics , statistics , theoretical computer science , physics , quantum mechanics , operating system
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.