Abstract
This paper reviews tests for structural change in linear regression models from the
generalized fluctuation test framework as well as from the F test (Chow test) framework.
It introduces a unified approach for implementing these tests and presents how these ideas
have been realized in an R package called strucchange. Enhancing the standard significance
test approach the package contains methods to fit, plot and test empirical fluctuation
processes (like CUSUM, MOSUM and estimates-based processes) and to compute, plot and
test sequences of F statistics with the supF, aveF and expF test. Thus, it makes powerful
tools available to display information about structural changes in regression relationships
and to assess their significance. Furthermore, it is described how incoming data can be
monitored.
generalized fluctuation test framework as well as from the F test (Chow test) framework.
It introduces a unified approach for implementing these tests and presents how these ideas
have been realized in an R package called strucchange. Enhancing the standard significance
test approach the package contains methods to fit, plot and test empirical fluctuation
processes (like CUSUM, MOSUM and estimates-based processes) and to compute, plot and
test sequences of F statistics with the supF, aveF and expF test. Thus, it makes powerful
tools available to display information about structural changes in regression relationships
and to assess their significance. Furthermore, it is described how incoming data can be
monitored.
Originalsprache | Englisch |
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Fachzeitschrift | Journal of Statistical Software |
Publikationsstatus | Veröffentlicht - 2002 |