On Mixture Double Autoregressive Time Series Models
提出混合双自回归模型，允许混合比例随时间变化，推导了严格平稳性和高阶矩性质，并研究了极大似然估计、EM算法和模型选择准则，蒙特卡洛实验和实证分析支持了新模型。
This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the literature. To make it more flexible, the mixing proportions are further assumed to be time varying, and probabilistic properties including strict stationarity and higher order moments are derived. Inference tools including the maximum likelihood estimation, an expectation–maximization (EM) algorithm for searching the estimator and an information criterion for model selection are carefully studied for the logistic mixture double autoregressive model, which has two components and is encountered more frequently in practice. Monte Carlo experiments give further support to the new models, and the analysis of an empirical example is also reported.