My problem is the following:
I have a model forecasting the sales of a certain brand. In period 4 a strike caused the sales to decrease. I want to know whether this strike has caused the sales to decrease permanently.
What I first thought to do is to split up the sample in a pre-strike period and post-strike period. If then the pre-strike period is stationary and the post-strike period has a unit-root, the effect is permanent (in the presence of a unit root, a shock will cause a long-term effect on the dependent variable). But according to Perron(1989) this procedure has low power.
Perron (1989) talks about a structural-break unit-root test. Does somebody know whether this test might solve my problem and if not, how I can possibly tackle this problem?
Many thanks in advance.
Best Answer
In the presence of a unit root, a shock will cause a long-term effect on the dependent variable does not imply If then the pre-strike period is stationary and the post-strike period has a unit-root, the effect is permanent.
A simple way to solve your problem (part of it was already suggested in the comments):
auto.arima()
in packageforecast
inR
.)Why couldn't you just use a simple t-test for equality of the means in the two subsamples? Because the standard t-test requires the data points to be independent. Meanwhile, the data points in each subsample are most likely not independent of each other as your data is a time series.