State Space Models With a Common Stochastic Variance
结合线性高斯状态空间模型与随机波动率模型，提出一种同时估计均值和方差过程参数的卡尔曼滤波与蒙特卡洛最大似然方法，并通过美国月度通胀率数据展示建模与预测效果。
This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman filter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properties of maximum likelihood estimators apply as a result. A Monte Carlo simulation study is carried out to investigate the small-sample properties of the estimation procedure. The modeling and forecasting techniques within our new framework are illustrated for U.S. monthly inflation rates.