Pairwise likelihood estimation of latent autoregressive count models

TL;DR

This paper introduces a weighted pairwise likelihood method for latent autoregressive count models, offering a computationally efficient alternative to simulation-based likelihood approximation in infectious disease time series analysis.

Contribution

It proposes a novel pairwise likelihood approach with techniques for robust standard error estimation and numerical integration, improving inference in latent autoregressive models.

Findings

Applied method to meningococcal disease data in Greece and Italy.

Demonstrated computational efficiency over simulation-based methods.

Provided practical guidance on numerical integration and standard error estimation.

Abstract

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.