  In this work we introduce the class of unit-Weibull Autoregressive Moving
Average models for continuous random variables taking values in $(0,1)$. The
proposed model is an observation driven one, for which, conditionally on a set
of covariates and the process' history, the random component is assumed to
follow a unit-Weibull distribution parameterized through its $\rho$th quantile.
The systematic component prescribes an ARMA-like structure to model the
conditional $\rho$th quantile by means of a link. Parameter estimation in the
proposed model is performed using partial maximum likelihood, for which we
provide closed formulas for the score vector and partial information matrix. We
also discuss some inferential tools, such as the construction of confidence
intervals, hypotheses testing, model selection, and forecasting. A Monte Carlo
simulation study is conducted to assess the finite sample performance of the
proposed partial maximum likelihood approach. Finally, we examine the
prediction power by contrasting our method with others in the literature using
the Manufacturing Capacity Utilization from the US.
