Timeline for Rolling autocorrelation vs whole series autocorrelation
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 29, 2020 at 14:33 | comment | added | Nik | @IrishStat thanks! Will read about this test | |
| May 29, 2020 at 14:32 | comment | added | Nik | @mlofton, thank you for your response! Still working on this problem so will try such strategy! | |
| Apr 19, 2020 at 19:13 | comment | added | IrishStat | there is an answer estimate the model for sub-groups and test for homogeneity of parameters using the 1960 CHOW TEST | |
| Apr 19, 2020 at 14:23 | comment | added | mlofton | This is a GENERAL problem with all time series modelling that most textbooks neglect to talk about: The instability of parameters of ANY KIND, be it the autocorrelations, coefficient estimates, etc. The best thing to do is make a decusion on your own about what you think will be most representative of the future. One month, one week, 6 months etc. This decision depends on what horizon you're trying to forecast. Are you trying to forecast monthly, daily, hourly etc. Basically, there is no answer to your good question. | |
| Apr 19, 2020 at 12:06 | comment | added | Nik | Let me clarify myself a little bit. I understand that ACF is not a model, what I mean is that when we calculate ACF for the whole series, we, for example, obtain insignificant autocorrelation at the first lag. But suppose when we calculate rolling autocorrelations we observe that many 1st lags are significant. Moreover, in many cases autocorrelation of the whole series is about 0 but rolling, for example, in my sample shows more-or-less 0.5. At the end of the day, I want to know which series show significant dependencies. Which of two cases leads to that? | |
| Apr 19, 2020 at 11:06 | history | answered | Haotian Chen | CC BY-SA 4.0 |