Quantifying the effect of classification errors on the accuracy of mixed-source statistics

Estimating the effect of non-sampling errors on the accuracy of mixed-source statistics is not straightforward. Here we simulate the bias and variance of the turnover estimates in car trade due to classification errors, using a bootstrap approach. In addition, we study the extent to which manual selective editing at micro level can improve the accuracy. We discuss how to develop a practical method that can be implemented in production to estimate the accuracy of register-based estimates.