Timeline for How to interpret these acf and pacf plots
Current License: CC BY-SA 3.0
12 events
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| Jun 7, 2015 at 2:13 | vote | accept | rnso | ||
| Apr 20, 2015 at 15:34 | comment | added | IrishStat | @javlacalle see my last comment regarding seasonality in the data | |
| Apr 20, 2015 at 1:19 | comment | added | IrishStat | seasonality is present in the AR(12) term in the ARIMA model and in the seasonal pulse starting at period 98 (2003/2) | |
| Apr 19, 2015 at 23:04 | comment | added | javlacalle | (+1) Nice analysis of the data. However, what about the original question? can seasonality be identified in the data? Maybe it can be inferred from the output that you show, but I couldn't figure it out. | |
| Apr 19, 2015 at 13:04 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Apr 19, 2015 at 11:51 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Apr 19, 2015 at 11:45 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Apr 19, 2015 at 11:29 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Apr 19, 2015 at 10:25 | comment | added | IrishStat | To reiterate following my friend stats.stackexchange.com/users/48766/javlacalle : Checking for the presence of pulses and level shifts AND seasonal pulses AND local time trends AND error variance constancy is also necessary. | |
| Apr 19, 2015 at 9:47 | comment | added | IrishStat | I hate to say never BUT the assumptions that I laid out would severely complicate the visual identification process if violated. Your data set clearly ( to my old eyes ) is an example of this. Identifying an initial model , estimating and re-identifying based upon residual diagnostics is a multi-stage process not a one and done EXCEPT in trivial cases. | |
| Apr 19, 2015 at 2:06 | comment | added | rnso | Thanks for your answer. Are these assumptions so important and always so flouted in real world data that acf and pacf plots can almost never be interpreted in isolation? | |
| Apr 18, 2015 at 18:43 | history | answered | IrishStat | CC BY-SA 3.0 |