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arXiv · 2201.11053

Improved Maximum Likelihood Estimation of ARMA Models

Leonardo Di Gangi·Matteo Lapucci·Fabio Schoen·Alessio Sortino
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Abstract

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.

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Leonardo Di Gangi, Matteo Lapucci, Fabio Schoen, Alessio Sortino. 2022-01-26. Improved Maximum Likelihood Estimation of ARMA Models. https://arxiv.org/abs/2201.11053

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