Granger Causal Priority and Choice of Variables in Vector Autoregressions
研究者在用向量自回归建模一组核心变量时，如何从更多候选变量中挑选额外变量。本文提出基于格兰杰因果优先性的贝叶斯方法，并给出后验概率的闭式表达式，应用于美国与欧元区的产出、价格和利率数据得到相似结果。
A researcher is interested in a set of variables that he wants to model with a vector auto-regression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in prediction and impulse response analysis. We develop a Bayesian methodology to answer this question. We rely on the idea of Granger-causal-priority, related to the well-known concept of Granger-non-causality. The methodology is simple to use, because we provide closed-form expressions for the relevant posterior probabilities. Applying the methodology to the case when the variables of interest are output, the price level, and the short-term interest rate, we find remarkably similar results for the United States and the euro area.