Beta autoregressive moving average models

We build upon the class of beta regressions introduced by Ferrari and Cribari-Neto (J. Appl. Stat. 31:799-815, 2004) to propose a dynamic model for continuous random variates that assume values in the standard unit interval (0,1). The proposed beta ARMA model includes both autoregressive and moving average dynamics, and also includes a set of regressors. We discuss parameter estimation, hypothesis testing, goodness-of-fit assessment and forecasting. In particular, we give closed-form expressions for the score function and for Fisher's information matrix. An application that uses real data is presented and discussed.