Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autore...

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英文文献：Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach-使用因子增强向量自回归估计美国货币政策冲击:一种新兴市场算法方法
英文文献作者：Lasse Bork
英文文献摘要：
Economy-wide effects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR) by Bernanke et al. (2005). I estimate the FAVAR by the fully parametric one-step EM algorithm as an alternative to the two-step principal component method and the one-step Bayesian method in Bernanke et al. (2005). The EM algorithm which is an iterative maximum likelihood method estimates all the parameters and the dynamic factors simultaneously and allows for classical inference. I demonstrate empirically that the same impulse responses but better fit emerge robustly from a low order FAVAR with eight correlated factors compared to a high order FAVAR with fewer correlated factors, for instance four factors. This empirical result accords with one of the theoretical results from Bai & Ng (2007) in which it is shown that the information in complicated factor dynamics may be substituted by panel information.

在一个状态空间模型中，由联邦基金利率的动态和一些动态因素驱动的120个美国宏观经济和金融时间序列估计了冲击对美国联邦基金利率的整体经济影响。Bernanke等人(2005)将这种状态空间系统表示为因子扩增VAR (FAVAR)。我使用完全参数一步EM算法来估计FAVAR，作为Bernanke等人(2005)中两步主成分法和一步贝叶斯法的替代方法。EM算法是一种迭代极大似然法，同时估计所有参数和动态因素，并允许经典推理。我通过实验证明，具有8个相关因子的低阶FAVAR与具有较少相关因子(例如4个)的高阶FAVAR相比，具有相同的脉冲响应但更好的拟合性。这一实证结果与Bai & Ng(2007)的理论结果一致，该理论结果表明，复杂因素动力学中的信息可以被面板信息所替代。

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