The Asymptotic Behaviour of the Residual Sum of Squares in Models with Multiple Break Points

Alastair R Hall, Denise R. Osborn, Nikolaos Sakkas

Research output: Working paper

121 Downloads (Pure)


Models with multiple discrete breaks in parameters are usually estimated via least squares. This paper, firstly, derives the asymptotic expectation of the residual sum of squares, the form of which indicates that the number of estimated break points and the number of regression parameters affect the expectation in different ways. Secondly, we propose a statistic for testing the joint hypothesis that the breaks occur at specified points in the sample and show that the statistic hasa limiting null distribution that is non-standard but simulatable. In an important special case, the statistic can be normalized to make it pivotal and we provide percentiles for the associated limiting distribution. Our analytical results cover linear and nonlinear regression models with exogenous regressors estimated via Ordinary (or Nonlinear) Least Squares and a linear model in which some regressors are endogenous and the model is estimated via Two Stage Least Squares. An application to US monetary policy rejects the common assumption that identified breaks are associated with changes in the chair of the Fed.
Original languageEnglish
Place of PublicationManchester
Number of pages50
Publication statusPublished - Mar 2015

Publication series

NameEconomics discussion paper series
PublisherUniversity of Manchester


  • Linear models, Nonlinear models, Ordinary Least Squares, Two-Stage Least Squares, Parameter change, US monetray policy


Dive into the research topics of 'The Asymptotic Behaviour of the Residual Sum of Squares in Models with Multiple Break Points'. Together they form a unique fingerprint.

Cite this