Nonlinear Regime-Switching State-Space (RSSS) Models
Nonlinear Regime-Switching State-Space (RSSS) Models
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DOI：
10.1007/s11336-013-9330-8
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发表时间：
2013-10-01
期刊：
影响因子：
3
通讯作者：
Zhang, Guangjian
中科院分区：
文献类型：
--
作者：
Chow, Sy-Miin;Zhang, Guangjian
非线性动态因子分析模型通过允许时间序列过程在潜在水平（例如，涉及两个潜在过程之间的相互作用）。在实践中，它往往是感兴趣的阶段，即确定潜在的“政权”或类，在此期间，一个系统的特点是明显不同的动态。我们提出了一类新的模型，称为非线性状态切换状态空间（RSSS）模型，其中包括状态切换非线性动态因子分析模型作为一种特殊情况。在非线性RSSS模型中，允许状态空间模型中的变化过程是非线性的。提出了一种将扩展卡尔曼滤波和Kim滤波相结合的方法来估计非线性RSSS模型。我们通过将具有特定于制度的交叉回归参数的非线性动态因子分析模型拟合到一组经验抽样影响数据来说明非线性RSSS模型的实用性。简要讨论了非线性RSSS模型与文献中其他著名离散变化模型的相似之处。
Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases-namely, latent "regimes" or classes-during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.