Least Squares Estimation in Multiple Change-Point Models

Change-point analysis is devoted to the detection and estimation of the time of structural changes within a data set of time-ordered observations. In this thesis, we estimate simultaneously multiple change-points by the least squares method and examine asymptotic properties of such estimators. Using argmin theorems, we prove weak and strong consistency under different moment conditions and investigate convergence in distribution. The identification of the limit variable allows us to derive an asymptotic confidence region for the unknown parameters. Based on a simulation study we evaluate these results