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I have a general question about Bayesian Regression Modeling and how a prior might be used as a means to control for (close to) simultaneous events. I often face a situation where I have a time series of sales and am looking to estimate the impact of an event (on the trend and level). This would be a candidate for an interrupted time series model - encoding periods post the event as 1 and 0 prior. The problem is that there are often other events that have not happened before, in close proximity time wise to the event of interest - maybe at the same time for all or part of the same time as the event of interest.

If I am willing to use ‘side information’ (maybe from subject matter experts or analysis in another region) …can I use a strong informative prior to ‘control’ for these other events and estimate the one I care most about?

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    $\begingroup$ It would be possible provided highly informative prior, I’m not a Bayesian expert, but see here. I would search for “multi colinearity and Bayesian regression” which is what you have. $\endgroup$
    – forecaster
    Commented Dec 3, 2020 at 2:34
  • $\begingroup$ I am not so sure but I have a feeling that it might be better if you could write down the model that you are using along with the parameters that you are focusing on. $\endgroup$
    – TrungDung
    Commented Dec 15, 2020 at 18:29
  • $\begingroup$ I'm not sure what your idea with the priors is, but from your problem description I wonder if change-point models would be right for. $\endgroup$
    – Durden
    Commented Jun 24, 2023 at 20:47

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