Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa
设计了一个贝叶斯动态潜因子模型，用于分析爱荷华州经济数据，通过马尔可夫链蒙特卡洛方法估计参数，并利用潜因子的后验均值构建同步与先行指标。
This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are given by posterior mean values of current and predictive distributions for the latent factor.