My apologies if this has been addressed before, however my experience with statistics is a bit limited, particularly when it comes to time series analyses.
My current question relates to the analysis of a trend in monthly sales before and after an event for a single product. Suppose there is a new product with increasing monthly sales, however the company decides to initiate a promotion 12 months after it launches to further increase sales. A year later, the company wants to examine if the sales trend after the promotion is significantly different from the trend prior to the promotion (ideally the unit sales would be increasing at a faster rate after initiation of the promotion). So essentially, I want to determine if I had forecasted 12 months into the future just prior to the promotion, are the sales significantly different from what they actually turned out to be.
What statistical test is the most appropriate to use to determine if the monthly sales trend is significantly different before vs. after the promotion? I only have one data point for each time point (monthly sales) before and after, and since the sales are increasing I have come across problems due to the "stationarity" requirement for many tests, as well as the fact that I would only have 12 historical data points on which to base the initial forecast.
Best Answer
Would a time series intervention analysis suit your needs? It estimates how much an intervention has changed a time series, if at all.
how to in R: http://www.r-bloggers.com/time-series-intervention-analysis-wih-r-and-sas/
example use case: What test should I use to determine if a policy change had a statistically significant impact on website registrations?
online course notes: https://onlinecourses.science.psu.edu/stat510/?q=node/76