I am using seasonally adjusted sales as my dependent variable.
For each store, i have about 4 years of data. The accuracy looks good in the training and validation datasets but my concern is that the random effect estimates are unstable.
When you talk about ignoring the random effects with high standard errors, how should I do that? I have lag12(sales) and intercept as random effects. For some stores, these estimates are pretty much stable, for some stores they are way off. Are you talking about ignoring the variables for the model or for some stores only? Should I ignore or use the random effect estimates in deriving the predicted sales for those stores where the estimate is insignificant or has a high standard error?
Thanks,
Datalligence