Quantifying the Federal Reserve’s Objectives Using a Structural Vector Autoregressive Model

Shengliang Ou, Donghai Zhang
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Abstract

The Federal Reserve’s (Fed’s) objective, namely, its dovish stance, is often blamed for the so-called Great Inflation. A popular proxy for the former is constructed using the inflation coefficients in estimated Taylor rules. However, for a welfare-optimizing central bank, the estimated Taylor coefficients are not sufficient for inferring its underlying preference. We quantify Fed’s objective—the targeting rule—relying on a conditional estimator (Galí and Gambetti 2018) that is free of the classical simultaneity problem. We discover that Fed’s targeting rule remained stable during the pre- and post-Volcker periods—the opposite of what is implied through a Taylor rule estimation.
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用结构向量自回归模型量化美联储的目标
美联储(Fed)的目标,即其鸽派立场,经常被指责为所谓的大通胀的罪魁祸首。前者的一个流行的代理是使用估计泰勒规则中的膨胀系数构造的。然而,对于福利优化的中央银行,估计的泰勒系数不足以推断其潜在偏好。我们量化了美联储的目标——目标规则——依赖于一个不存在经典同时性问题的条件估计器(Galí和Gambetti 2018)。我们发现,美联储的目标规则在沃尔克之前和之后的时期保持稳定,这与泰勒规则估计所暗示的相反。
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