Tailored randomized block MCMC methods with application to DSGE models
适配随机分块 MCMC 方法及其在 DSGE 模型中的应用
In this paper we develop new Markov chain Monte Carlo schemes for the estimation of Bayesian models. One key feature of our method, which we call the tailored randomized block Metropolis-Hastings (TaRB-MH) method, is the random clustering of the parameters at every iteration into an arbitrary number of blocks. Then each block is sequentially updated through an M-H step. Another feature is that the proposal density for each block is tailored to the location and curvature of…