Abstract
An approach to the analysis of data that contains (multiple) structural changes in a linear
regression setup is presented. Various strategies which have been suggested in the literature for
testing against structural changes as well as a dynamic programming algorithm for the dating
of the breakpoints are implemented in the R statistical software package. Using historical data
on Nile river discharges, road casualties in Great Britain and oil prices in Germany it is shown
that statistically detected changes in the mean of a time series as well as in the coefficients
of a linear regression coincide with identifiable historical, political or economic events which
might have caused these breaks.
regression setup is presented. Various strategies which have been suggested in the literature for
testing against structural changes as well as a dynamic programming algorithm for the dating
of the breakpoints are implemented in the R statistical software package. Using historical data
on Nile river discharges, road casualties in Great Britain and oil prices in Germany it is shown
that statistically detected changes in the mean of a time series as well as in the coefficients
of a linear regression coincide with identifiable historical, political or economic events which
might have caused these breaks.
Originalsprache | Englisch |
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Seiten (von - bis) | 109 - 123 |
Fachzeitschrift | Computational Statistics and Data Analysis |
Jahrgang | 44 |
Ausgabenummer | 1-2 |
Publikationsstatus | Veröffentlicht - 2003 |