Timeline for How to detect an AR(1) process of residuals from a correlogram?
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 18, 2022 at 17:26 | comment | added | Geek_Tech | Let us continue this discussion in chat. | |
| Jan 18, 2022 at 6:37 | comment | added | Richard Hardy | @Geek_Tech, you are welcome! | |
| Jan 18, 2022 at 2:01 | comment | added | Geek_Tech | thank you so much for the explanation. | |
| Jan 18, 2022 at 2:01 | vote | accept | Geek_Tech | ||
| Jan 17, 2022 at 19:44 | comment | added | Richard Hardy | @Geek_Tech, what is DB statistic? Durbin-Watson (DW)? DW assesses first-order autocorrelation (AC1), but that is allowed in your model, so we do not care whether AC1 is zero or not. What you need is ACs beyond the first order to see if they are different from zero. That could be tested using Breusch-Godfrey or perhaps Ljung-Box. But a formal test will not tell you more than we already see from the graphs... | |
| Jan 17, 2022 at 19:38 | comment | added | Geek_Tech | The Durbin Watson statistic was around 2.466. Does it provide any justification? When the residuals are allowed to follow an AR(1) process, papers I referred do not show any residual diagnostics. I am bit confused so. | |
| Jan 17, 2022 at 19:22 | comment | added | Richard Hardy | @Geek_Tech, I guess I do not have a satisfactory answer. I would keep the model as is for practical use, but that would also entail risking that some picky reviewers would criticize the reliability of the results based on the residual diagnostics. | |
| Jan 17, 2022 at 19:20 | comment | added | Richard Hardy | @Geek_Tech, it is a subtle matter. We are looking for a good balance between underfitting (leaving genuine patterns of a population unaccounted for in our model) and overfitting (accounting for sample-specific noise variation that is not to be found in the population). Yes, sound inference requires assumptions to be met, but an overfitted model represents noise in addition to the genuine patterns even though the model appears adequate when looking at its residuals. A middle ground is to account for the large (in the subject-matter sense) patterns but leave the small ones unaccounted for. | |
| Jan 17, 2022 at 14:27 | comment | added | Geek_Tech | thank you for the explanation Professor. So according to your opinion, is it better to leave the model as it is? Also, you have mentioned "depending on how sensitive to marginally significant ACs and PACs you want to be". ..In order to justify my model is adequate with residuals following an AR(1) process, how should I address the above-mentioned statement? | |
| Jan 17, 2022 at 14:17 | history | answered | Richard Hardy | CC BY-SA 4.0 |