It is well known that in the presence of equation error in the regression models any estimator based on assumed knowledge of the ratio of error variances () is biased. Although Wald’s method could deal with models that include equation error, it lacks efficiency and subject to identifiability problem. Based on the above two methods this paper introduces a new slope estimator for regression model when both variables are subject to measurement errors and the model includes equation error. The main aim of the proposed method is to improve the efficiency of the Wald’s estimator under flexible assumption on ratio of error variance. To compare the relative efficiency of the proposed estimator with the OLS, Wald’s and Geary’s estimators simulation studies under various assumptions are undertaken. Also included a comparison of the new estimator with the method of moments estimator when  is biased due to the presence of the equation error. The simulation results show that the proposed estimator is more consistent and efficient than the other estimators.