|Start Page Number:||1055|
|End Page Number:||1069|
|Publication Date:||Oct 2017|
|Authors:||Ching Wai-Ki, Siu Tak-Kuen, Zhu Dong-Mei, Elliott Robert, Zhang Lianmin|
|Keywords:||markov processes, probability, stochastic processes, heuristics, optimization, differential equations|
In this paper, we propose a higher‐order interactive hidden Markov model, which incorporates both the feedback effects of observable states on hidden states and their mutual long‐term dependence. The key idea of this model is to assume the probability laws governing both the observable and hidden states can be written as a pair of higher‐order stochastic difference equations. We also present an efficient procedure, a heuristic algorithm, to estimate the hidden states of the chain and the model parameters. Real applications in SSE Composite Index data and default data are given to demonstrate the effectiveness of our proposed model and corresponding estimation method.