@article {8918,
	title = {Positive time series regression models: theoretical and computational aspects},
	year = {2024},
	note = {n/a},
	month = {2024},
	abstract = {<p>This paper discusses dynamic ARMA-type regression models for positive time series, which can handle bounded non-Gaussian time series without requiring data transformations. Our proposed model includes a conditional mean modeled by a dynamic structure containing autoregressive and moving average terms, time-varying covariates, unknown parameters, and link functions. Additionally, we present the PTSR package and discuss partial maximum likelihood estimation, asymptotic theory, hypothesis testing inference, diagnostic analysis, and forecasting for a variety of regression-based dynamic models for positive time series. A Monte Carlo simulation and a real data application are provided.</p>
},
	isbn = {1613-9658},
	url = {https://doi.org/10.1007/s00180-024-01531-z},
	author = {Prass, Taiane Schaedler and Pumi, Guilherme and Taufemback, Cleiton Guollo and Carlos, Jonas Hendler}
}
