Approximate state space modelling of unobserved fractional components
提出一种基于有限阶ARMA近似的状态空间方法，用于估计可能非平稳的多元分数积分与协整未观测成分模型，并通过蒙特卡洛模拟验证其良好性能。
We propose convenient inferential methods for potentially nonstationary multivariate unobserved components models with fractional integration and cointegration. Based on finite-order ARMA approximations in the state space representation, maximum likelihood estimation can make use of the EM algorithm and related techniques. The approximation outperforms the frequently used autoregressive or moving average truncation, both in terms of computational costs and with respect to approximation quality. Monte Carlo simulations reveal good estimation properties of the proposed methods for processes of different complexity and dimension.