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Abstract

Models with multiple discrete breaks in parameters are usually estimated via least squares. This paper, first, derives the asymptotic expectation of the residual sum of squares and shows that the number of estimated break points and the number of regression parameters affect the expectation differently. Second, we propose a statistic for testing the joint hypothesis that the breaks occur at specified points in the sample. Our analytical results cover models estimated by the ordinary, nonlinear, and two-stage least squares. An application to U.S. monetary policy rejects the assumption that breaks are associated with changes in the chair of the Fed.

Item Type:	Article
Authors/Creators:	Hall, AR and Osborn, DR and Sakkas, N
Keywords:	linear models, nonlinear models, ordinary least squares, parameter change, two-stage least squares, US monetary policy
Journal or Publication Title:	Econometric Reviews
Publisher:	Taylor & Francis Inc.
ISSN:	0747-4938
DOI / ID Number:	10.1080/07474938.2017.1307523
Copyright Information:	Copyright 2017 Alastair R. Hall, Denise R. Osborn, and Nikolaos Sakkas.Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
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