论文标题
具有动态因子模型的脉冲反应函数的估计：一个新的参数化
Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization
论文作者
论文摘要
我们为动态因子模型（DFMS）的脉冲反应函数（IRF）的估计和识别提出了一个新的参数化。本文的理论贡献涉及不同IRF之间观察等同的问题，这意味着未识别IRF参数而没有进一步的限制。我们展示了以前提出的最小识别条件是如何嵌套在新框架中的，并且可以通过过度识别限制进一步增强，从而导致效率提高。 IRF估计DFM的当前标准实践基于主组件，与新的参数化相比，限制性较低，并允许建模更丰富的动力学。作为本文的经验贡献，我们基于EM算法制定了一种估计方法，该方法包含了拟议的识别限制。在经验应用中，我们使用标准的高维宏观经济数据集来估计货币政策冲击的影响。我们估计宏观经济变量的强烈反应，而基准模型似乎给出了定性的违反直觉结果。估计方法在随附的R软件包中实现。
We propose a new parametrization for the estimation and identification of the impulse-response functions (IRFs) of dynamic factor models (DFMs). The theoretical contribution of this paper concerns the problem of observational equivalence between different IRFs, which implies non-identification of the IRF parameters without further restrictions. We show how the previously proposed minimal identification conditions are nested in the new framework and can be further augmented with overidentifying restrictions leading to efficiency gains. The current standard practice for the IRF estimation of DFMs is based on principal components, compared to which the new parametrization is less restrictive and allows for modelling richer dynamics. As the empirical contribution of the paper, we develop an estimation method based on the EM algorithm, which incorporates the proposed identification restrictions. In the empirical application, we use a standard high-dimensional macroeconomic dataset to estimate the effects of a monetary policy shock. We estimate a strong reaction of the macroeconomic variables, while the benchmark models appear to give qualitatively counterintuitive results. The estimation methods are implemented in the accompanying R package.
