Open Access
February 2018 A MOSUM procedure for the estimation of multiple random change points
Birte Eichinger, Claudia Kirch
Bernoulli 24(1): 526-564 (February 2018). DOI: 10.3150/16-BEJ887


In this work, we investigate statistical properties of change point estimators based on moving sum statistics. We extend results for testing in a classical situation with multiple deterministic change points by allowing for random exogenous change points that arise in Hidden Markov or regime switching models among others. To this end, we consider a multiple mean change model with possible time series errors and prove that the number and location of change points are estimated consistently by this procedure. Additionally, we derive rates of convergence for the estimation of the location of the change points and show that these rates are strict by deriving the limit distribution of properly scaled estimators. Because the small sample behavior depends crucially on how the asymptotic (long-run) variance of the error sequence is estimated, we propose to use moving sum type estimators for the (long-run) variance and derive their asymptotic properties. While they do not estimate the variance consistently at every point in time, they can still be used to consistently estimate the number and location of the changes. In fact, this inconsistency can even lead to more precise estimators for the change points. Finally, some simulations illustrate the behavior of the estimators in small samples showing that its performance is very good compared to existing methods.


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Birte Eichinger. Claudia Kirch. "A MOSUM procedure for the estimation of multiple random change points." Bernoulli 24 (1) 526 - 564, February 2018.


Received: 1 August 2015; Revised: 1 July 2016; Published: February 2018
First available in Project Euclid: 27 July 2017

zbMATH: 06778339
MathSciNet: MR3706768
Digital Object Identifier: 10.3150/16-BEJ887

Keywords: binary segmentation , change point , Hidden Markov model , moving sum statistics , regime switching model

Rights: Copyright © 2018 Bernoulli Society for Mathematical Statistics and Probability

Vol.24 • No. 1 • February 2018
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