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I am using the table below as model selection tool (at least as starting point)

enter image description here

Let's say that I choose a proper model according to the table and I get nice ACF and PACF out of it, but either my AR term or my MA term is pretty high, is there a way to simplify it?

Note: I don't know if it is relevant, but I am using R.

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  • $\begingroup$ .......... see my answer $\endgroup$
    – IrishStat
    Commented Nov 15, 2017 at 12:02
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    $\begingroup$ What do you mean by "either my AR term or my MA term is pretty high"? I assume you mean the AR or MA orders? This can happen with seasonality (and then you should be doing seasonal differencing), but otherwise very rarely. $\endgroup$
    – Stephan Kolassa
    Commented Nov 15, 2017 at 12:04
  • $\begingroup$ Or perhaps I actually misunderstood the question... Could you clarify? $\endgroup$
    – Firebug
    Commented Nov 15, 2017 at 12:27
  • $\begingroup$ My question is, if after an iterative process I find a proper model but the AR or MA order (or both) is pretty high, should I consider a way to simply them? If yes, how should I procede? $\endgroup$
    – user158013
    Commented Nov 15, 2017 at 12:34
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    $\begingroup$ Could you try to make your title more specific? And also explain what you mean by "AR term or my MA term is pretty high"? $\endgroup$
    – Richard Hardy
    Commented Nov 15, 2017 at 12:37

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keep in mind that there is a presumption of no pulses, no level shifts , no seasonal pulses and no local time trends in your approach. If you treat the identification in a holistic manner , the ARIMA structure is often quite simplified and more correct. Additional assumptions are that both the parameters and the model error variance are constant over time. One has to read/understand the fine print.

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    $\begingroup$ I am not sure I totally understood your answer: but my question is not actually about the selection method; it's more about wheter or not I should take for good a model with high AR or MA order or if I should argue them somehow. $\endgroup$
    – user158013
    Commented Nov 15, 2017 at 12:34
  • $\begingroup$ i would argue them by starting simple and proceed with an iterative self-checking process to build up the model to a point of sufficiency. The software/approach you are using to do model identification is probably way over-parameterizing. If you wish to post a data set and your model I will try and help further. $\endgroup$
    – IrishStat
    Commented Nov 15, 2017 at 12:41

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