In the textbook Forecasting: principles and practice by Hyndman and Athanasopoulos, in the Classical Decomposition (Sec 6.3), in step 3 of the additive decomposition algorithm, the authors state that the seasonal indexes have to be adjusted to ensure that they add to zero. Why is that?
Any help would be appreciated.
Contents
hide
Best Answer
Just to make coefficients meaningful. Suppose you had quarterly data and a pattern within the year. If you include a year effect the seasonal coefficients can be interpreted as average deviations from the year average, and as such should add up to zero. It is just the same reason why ANOVA effects are made to add up to zero as well.
Similar Posts:
- Solved – Lambda value for BoxCox transformation in time series analysis
- Solved – I have correlogram ACF and PACF below for a temperature time series. Can I say it is MA(2) from ACF? What about AR
- Solved – Free econometrics textbooks
- Solved – Seasonal decomposition or Holt-Winters methods for forecasting
- Solved – Seasonal decomposition or Holt-Winters methods for forecasting