Timeline for Structural change analysis: serial F-statistics test on raw or differenced data?
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| May 1, 2018 at 0:11 | vote | accept | dbspon | ||
| May 1, 2018 at 0:02 | comment | added | Achim Zeileis |
I would guess that r <- diff(log(y)) is the most intuitive transformation here, corresponding to returns or relative changes. And then bp <- breakpoints(r ~ 1) might pick up the two changes your are after. And then coef(bp) might show a first segment with positive growth, a second segment with negative growth, and a fairly stable third segment. Alternatively, you could try bp <- breakpoints(log(y) ~ seq_along(y), h = 4) where the segment-specific slopes in the deterministic trends should be similar to the segment-specific intercepts from the model in returns.
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| Apr 30, 2018 at 1:34 | comment | added | dbspon | ...for these data, the hypothesis of stationarity with level shifts is not definitely plausible. I'm assuming that my generating process is a pattern of shifting deterministic trends. I suppose that would mean modeling "returns" as diff(y) ~ 1? | |
| Apr 30, 2018 at 1:23 | comment | added | dbspon | Thanks for the feedback! Yes, this is the same data set that I illustrated in my related post. The data refer to pesticide application rates, and the changes in the shape correspond to documented shifts in agricultural practices. For example, the sharp declining trend beginning around t = 5 corresponds to a regulatory push to phase out two widely used chemical classes, and the weaker decline around t = 10 corresponds to a novel chemistry being widely adopted. If I may ask for clarification on some of your terms, | |
| Apr 28, 2018 at 11:17 | history | answered | Achim Zeileis | CC BY-SA 3.0 |