Block bootstrap for dependent errors-in-variables

Alinear errors-in-variables (EIV) model that contains measurement errors in the input and output data is considered. Weakly dependent (- and phi-mixing) errors, not necessarily stationary nor identically distributed, are taken into account within the EIV model. Parameters of the EIV model are estimated by the total least squares approach, which provides highly non linear estimates. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied. One possible solution to this dilemma is a block bootstrap. Anappropriate moving block bootstrap procedure is provided and its correctness proved. The results are illustrated through asimulation study and applied on real data as well.