Joint Bayesian Analysis of Parameters and States in Nonlinear non‐Gaussian State Space Models
提出一种新方法，为非线性非高斯状态空间模型中的参数和状态设计灵活的提议密度，用于独立Metropolis-Hastings算法或重要性采样，计算效率优于近期算法，并通过随机波动模型和实证数据验证。
Summary We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non‐Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis–Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic intensity and stochastic volatility models based on Ornstein–Uhlenbeck processes. For our empirical study, we analyse the performance of our methods for corporate default panel data and stock index returns. Copyright © 2016 John Wiley & Sons, Ltd.