用动态因子模型估计脉冲响应函数：一种新的参数化方法
Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization
摘要：
提出了一种新的参数化方法，用于动态因子模型（DFM）的脉冲响应函数（IRF）的估计和识别。本文的理论贡献涉及不同IRF之间的观测等效性问题，这意味着没有进一步的限制，IRF参数的非识别。我们展示了如何将先前提出的最小识别条件嵌套在新的框架中，并可以进一步增强过度识别限制，从而提高效率。目前的标准做法的国际RF估计的DFMs是基于主成分，相比之下，新的参数化是限制较少，并允许建模更丰富的动态。作为本文的经验贡献，我们开发了一种基于EM算法的估计方法，该方法结合了所提出的识别限制。在实证应用中，我们使用一个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违反直觉的结果。估计方法在随附的R包中实现。
英文摘要：
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.
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