Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
提出一种结合数值积分与重要性抽样的似然函数估计方法，用于高效处理高维状态向量和低维信号的非线性非高斯状态空间模型，在蒙特卡洛模拟和美股多因子随机波动率实证中均显著提升效率。
We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal can be evaluated more efficiently using the new method. We report many efficiency gains in an extensive Monte Carlo study as well as in an empirical application using a stochastic volatility model for U.S. stock returns with multiple volatility factors. Supplementary materials for this article are available online.