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英文文献：Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints-离散先验和数据驱动识别约束下DSGE模型的估计
英文文献作者：Markku Lanne,Jani Luoto
英文文献摘要：
We propose a sequential Monte Carlo (SMC) method augmented with an importance sampling step for estimation of DSGE models. In addition to being theoretically well motivated, the new method facilitates the assessment of estimation accuracy. Furthermore, in order to alleviate the problem of multimodal posterior distributions due to poor identification of DSGE models when uninformative prior distributions are assumed, we recommend imposing data-driven identification constraints and devise a procedure for finding them. An empirical application to the Smets-Wouters (2007) model demonstrates the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out-of-sample forecast comparisons as well as Bayes factors lend support to the constrained model.
提出了一种基于重要抽样步骤的序贯蒙特卡罗方法来估计DSGE模型。除了在理论上具有良好的动机外，新方法还有助于评估估计精度。此外，为了缓解在假设非信息先验分布时，由于DSGE模型识别能力差而导致的多模态后验分布问题，我们建议施加数据驱动的识别约束，并设计一个寻找这些约束的程序。Smets-Wouters(2007)模型的经验应用证明了估计方法的性质，并说明了如何通过识别约束消除参数冗余引起的多模态后验分布问题。样本外预测比较以及贝叶斯因子为约束模型提供了支持。
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