Revisiting inference for ARMA models: Improved fits and superior confidence intervals.

ABSTRACT: Autoregressive moving average (ARMA) models are widely used for analyzing time series data. However, standard likelihood-based inference methodology for ARMA models has avoidable limitations. We show that currently accepted standards for ARMA likelihood maximization frequently lead to sub-optimal parameter estimates. Existing algorithms have theoretical support, but can result in parameter estimates that correspond to a local optimum. While this possibility has been previously identified, it remains unknown to most users, and no routinely applicable algorithm has been developed to resolve the issue. We introduce a novel random initialization algorithm, designed to take advantage of the structure of the ARMA likelihood function, which overcomes these optimization problems. Additionally, we

SUBMITTER: Wheeler J 

PROVIDER: S-EPMC12551883 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature