Faster ARMA maximum likelihood estimation

TL;DR

This paper introduces a novel ARMA likelihood approximation that significantly reduces computational complexity, enabling efficient maximum likelihood estimation directly within high-level programming environments.

Contribution

A new AR approximation for ARMA likelihood computation that reduces complexity from O(n) to O(1), facilitating easier and faster MLE in high-level languages.

Findings

The new algorithm achieves results close to exact MLE.

It is easily implemented in environments like Mathematica, R, and MATLAB.

Simulation experiments demonstrate its effectiveness.

Abstract

A new likelihood based AR approximation is given for ARMA models. The usual algorithms for the computation of the likelihood of an ARMA model require $O(n)$ flops per function evaluation. Using our new approximation, an algorithm is developed which requires only $O(1)$ flops in repeated likelihood evaluations. In most cases, the new algorithm gives results identical to or very close to the exact maximum likelihood estimate (MLE). This algorithm is easily implemented in high level Quantitative Programming Environments (QPEs) such as {\it Mathematica\/}, MatLab and R. In order to obtain reasonable speed, previous ARMA maximum likelihood algorithms are usually implemented in C or some other machine efficient language. With our algorithm it is easy to do maximum likelihood estimation for long time series directly in the QPE of your choice. The new algorithm is extended to obtain the MLE for the mean parameter. Simulation experiments which illustrate the effectiveness of the new algorithm are discussed. {\it Mathematica\/} and R packages which implement the algorithm discussed in this paper are available (McLeod and Zhang, 2007). Based on these package implementations, it is expected that the interested researcher would be able to implement this algorithm in other QPE's.