Disclaimer: I am relatively new to time series analysis, so I am not sure if my way of thinking makes sense.
I have a time series of a few years, that I can model relatively well (respectable R squared, and normally distributed errors). However, in the last year of the data set, a series of effects happened that I am almost sure of had a large combined effect on the observed variable. Evidence of this is the fact that for this year, the residuals are no longer normally distributed and show a large upward bias.
I would like to capture this 'combined effect' in the model, by looking at the residuals. I am wondering whether it is possible to regard the residuals as a sum of a signal (of this combined effect) and noise, and to model it accordingly.
Is this a valid method?
