关键词: 状态空间模型, 时变参数估计, 变点分析, 粒子滤波

Abstract: There are numerous nonlinear time-vary time series in the field of macro-economy and financial capital market, however, the current study focus on mainly the static parameter state space model. The research improves the particle learning method for static parameter estimation, through introducing the change-point analysis, and proposes the change-point particle learning filter for the time-vary parameter estimation. Then, it compares the CPPL filter with the IMM filter via the random simulation trial, and the result shows that CPPL method has more advantages than the IMM filter in the parameter estimation for time-vary parameter state space model. The CPPL filter can be used to real-time track the simultaneous moving of stock price and trading volume.

Key words: State Space Model, Time-Vary Parameter Estimation, Change-Point Analysis, Particle Filter