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I'm hitting an issue with a causal impact model that I'm building.

I'm trying to create a counter factual for daily sales at one store (nseasons = 7). I've included sales for 5 other stores nearby. Eyeballing a lineplot, it appears to me that trends are similar across the 15 month period.

line plot

When I run my causal impact model, the CI bands are really wide.

Causal Impact Model

Any recommendations on what I can do to reduce the CI? Anything apart from adding more time series to the model? How big an issue is it to have wide CI in a Bayesian model (i.e. credible versus confidence)?

Here is the code:

CausalImpact(sales, pre.period, post.period, model.args = list(niter = 1000, nseasons = 7))

Any direction would be greatly appreciated!

adrianf22
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    This question might be a better fit for [Cross Validated](https://stats.stackexchange.com/). – Maurits Evers Apr 11 '18 at 01:46
  • Tag added - thanks! – adrianf22 Apr 11 '18 at 04:39
  • What I meant was that your question is better off at the sister site [https://stats.stackexchange.com/](https://stats.stackexchange.com/), which is called Cross Validated. SO is about specific coding questions, not about discussing statistical concepts. You're much more likely to find help for these kind of questions over at CV. – Maurits Evers Apr 11 '18 at 04:49
  • ah! understood - just posted there now. Thanks again for the help – adrianf22 Apr 11 '18 at 04:54

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