论文标题
改善了ARMA模型的最大似然估计
Improved Maximum Likelihood Estimation of ARMA Models
论文作者
论文摘要
在本文中，我们提出了一个新的优化模型，以最大程度地估计因果和可逆ARMA模型。通过一组数值实验，我们展示了我们所提出的模型在拟合模型的质量以及计算时间的质量方面如何优于表现，这是基于Jones Reparametrization的经典估计程序。我们还提出了该模型中的正规化项，并展示了这种添加如何改善拟合模型的样本质量。由于对接近非因果关系或非可逆性边界的模型的罚款增加，因此实现了这一改进。
In this paper we propose a new optimization model for maximum likelihood estimation of causal and invertible ARMA models. Through a set of numerical experiments we show how our proposed model outperforms, both in terms of quality of the fitted model as well as in the computational time, the classical estimation procedure based on Jones reparametrization. We also propose a regularization term in the model and we show how this addition improves the out of sample quality of the fitted model. This improvement is achieved thanks to an increased penalty on models close to the non causality or non invertibility boundary.
