Online estimation of DSGE models
展示了序贯蒙特卡洛方法在动态随机一般均衡模型后验分布估计中的实用性，包括自适应退火调度、在线估计的精度与速度优势，以及用在线估计计算伪样本外密度预测并检验先验分布变化对预测性能的影响。
Summary 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.