  In this paper we discuss dynamic ARMA-type regression models for time series
taking values in $(0,\infty)$. In the proposed model, the conditional mean is
modeled by a dynamic structure containing autoregressive and moving average
terms, time-varying regressors, unknown parameters and link functions. We
introduce the new class of models and discuss partial maximum likelihood
estimation, hypothesis testing inference, diagnostic analysis and forecasting.
