  Beta regression models provide an adequate approach for modeling continuous
outcomes limited to the interval (0,1). This paper deals with an extension of
beta regression models that allow for explanatory variables to be measured with
error. The structural approach, in which the covariates measured with error are
assumed to be random variables, is employed. Three estimation methods are
presented, namely maximum likelihood, maximum pseudo-likelihood and regression
calibration. Monte Carlo simulations are used to evaluate the performance of
the proposed estimators and the na\"ive estimator. Also, a residual analysis
for beta regression models with measurement errors is proposed. The results are
illustrated in a real data set.
