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how can one handle a time series with the Chow's test (in order to find a structural break) so that the assumption of independent model errors holds? I'm using the R function chow.test {gap}

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  • $\begingroup$ I am also interested in this question, as the autocorrelation will cause the Chow test to be more likely to detect structural breaks that don't actually exist, so a way to compensate for this would be very useful. $\endgroup$ Dec 19, 2013 at 13:21
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    $\begingroup$ I'v found an interesting article on this topic. The title is "The Chow test with serially correlated errors" by Isabella Consigliere. This article is archived in Jstor. For your reference, the detailed information of this article's publication is the following: Anno 89, No. 2 (aprile-giugno 1981), pp. 125-137 Published by: Vita e Pensiero – Pubblicazioni dell’Università Cattolica del Sacro Cuore Article Stable URL: jstor.org/stable/41624786 $\endgroup$ Jan 22, 2015 at 4:59

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I implemented the CHOW Test in AUTOBOX by forming an appropriate ARIMA structure and then simply using all estimated parameters (both regressor coefficients and ARIMA coefficients) in the test. In fact in the case of just 1 group/class this test is used to find the point in time where the greatest contrasts exists thus testing the hypothesis of constant parameters over time. You can do the same in R or any other programming language just be careful about outlier effects and possible non-constant error variance. See How can I programmatically detect segments of a data series to fit with different curves? for an application of the CHOW test to detect possible regime changes.

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    $\begingroup$ Congratulations on reaching 10,000 points, I have personally benefited on reading your posts on time series forecasting and outlier correction methods. $\endgroup$
    – forecaster
    Jun 14, 2015 at 1:01
  • $\begingroup$ I really appreciate your frankness and your very evident scholarship. $\endgroup$
    – IrishStat
    Jun 14, 2015 at 10:23

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