Seasonal lag
The lag one year back in a time-series model: four for quarterly data, twelve for monthly. A significant residual autocorrelation there calls for adding it to the model.
Where you will meet it
Seasonal lag appears in 3 learning outcomes on the CFA syllabus. A few of them:
- Explain how residual autocorrelations let you check whether an autoregressive model suits a time series
“A significant autocorrelation means the model is missing information: add lags, or a seasonal lag if the significant lag is four in quarterly data or twelve in monthly data, and test again.”
- Explain how to detect and adjust for seasonal effects in a time-series model, and work out and explain a forecast from an AR model that includes a seasonal lag
“Seasonality shows up as a significant residual autocorrelation at the seasonal lag, four for quarterly data and twelve for monthly data, after an AR model is fitted.”
- Choose a suitable time-series model for a given investment problem and give reasons for the choice
“Fit an AR(1) and test the residual autocorrelations, adding lags or a seasonal lag until none is significant.”
See also
Practise it
Practise seasonal lag until it is automatic: questions that explain every wrong option.
Take the free diagnostic →