Online estimation of DSGE models
Online estimation of DSGE models
复制标题
DSGE模型的在线估计
DOI：
10.1093/ectj/utaa029
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Schorfheide, Frank
中科院分区：
文献类型：
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作者：
Cai, Michael;Del Negro, Marco;Herbst, Edward;Matlin, Ethan;Sarfati, Reca;Schorfheide, Frank
本文阐述了序贯蒙特卡罗（SMC）方法在动态随机一般均衡（DSGE）模型后验分布近似中的作用。我们展示了如何自适应地选择回火时间表，记录广义数据回火的准确性和运行时的好处“在线”估计（即，重新估计新的数据成为可用的模型），并提供了多模态后验的例子，以及捕获SMC方法。然后，我们使用在线估计的DSGE模型来计算伪样本外的密度预测和研究的灵敏度的预测性能的先验分布的变化。我们发现，通过增加先验方差使先验信息量减少（与文献中使用的基准先验相比）不会导致预测准确性恶化。
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared with the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.