Bayes Estimation of GLARMA Models With Applications

This work presents a Bayesian approach for parameter estimation in the class of Generalized Linear Autoregressive Moving Average (GLARMA) models, extending the methodology beyond the common exponential family setting.

The proposed framework accommodates positive, double-bounded, and count time series through a unified MCMC-based estimation procedure implemented in \texttt{nimble}.

We discuss prior specifications for the model parameters and conduct an extensive Monte Carlo simulation study to evaluate the finite-sample performance of the approach under three distinct data-generating mechanisms: Negative Binomial-GLARMA for count data, Beta-GLARMA for double-bounded outcomes, and Gamma-GLARMA for positive continuous time series.

The simulation study assesses point and interval estimation, prior sensitivity, and the behaviour of the estimators under varying levels of temporal persistence, including challenging scenarios near the boundaries of the stationarity region.

The practical utility of the proposed framework is illustrated through two empirical applications: analysing monthly net electricity generation by nuclear plants in the United States using a Gamma-GLARMA model with harmonic seasonal components; and modelling monthly hospital admissions due to chronic obstructive pulmonary disease in Belo Horizonte, Brazil, using a Negative Binomial-GLARMA model with principal components derived from air pollution covariates.

The results demonstrate that the proposed Bayesian framework provides reliable and stable estimation across all settings, offering a flexible and practical tool for analysing non-Gaussian time series in a wide range of applications.