  In this work we introduce the class of beta autoregressive fractionally
integrated moving average models for continuous random variables taking values
in the continuous unit interval $(0,1)$. The proposed model accommodates a set
of regressors and a long-range dependent time series structure. We derive the
partial likelihood estimator for the parameters of the proposed model, obtain
the associated score vector and Fisher information matrix. We also prove the
consistency and asymptotic normality of the estimator under mild conditions.
Hypotheses testing, diagnostic tools and forecasting are also proposed. A Monte
Carlo simulation is considered to evaluate the finite sample performance of the
partial likelihood estimators and to study some of the proposed tests. An
empirical application is also presented and discussed.
