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.

中文翻译：

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在线估算DSGE模型

本文说明了顺序蒙特卡洛（SMC）方法在逼近动态随机一般均衡（DSGE）模型后验分布中的有用性。我们将展示如何自适应地选择调度时间表，记录用于“在线”估算（即在新数据可用时重新估算模型）的通用数据调度的准确性和运行时收益，并提供多模态后验的示例。可以很好地被SMC方法捕获。然后，我们使用DSGE模型的在线估计来计算伪样本外密度预测，并研究预测性能对先验分布变化的敏感性。