Object Reference : Object View and Procedure Reference : Series
  
 
stl
Seasonally adjust series using the STL decomposition method.
Unlike other seasonal adjustment methods used by EViews, this procedure works on any time frequency.
Syntax:
series.stl(options) seas_name[trend_name]
You should follow the stl keyword with a name for the seasonally adjusted series. Optionally, you may also provide a name for the output trend series.
Options
 
Periodicity=arg
Specify the periodicity. Use ā€œwā€ to expand weekly data to 53 weeks and ā€œdā€ to expand daily data to 366 (in a 7 day week workfile) or 261 (in a 5 day week workfile) days. Default is the number of periods per year (expanded for weekly and daily).
Sp=integer
Specify the seasonal polynomial degree. Default is 0.
tp=integer
Specify the trend polynomial degree. Default is 1.
fp=integer
Specify the filter polynomial degree. Default is 1.
Sl=integer
Specify the length of the seasonal smoothing window (odd integers only). Default is 35.
Tl=integer
Specify the length of the trend smoothing window (odd integers only). Default is based upon the seasonal smoothing window length.
Fl=integer
Specify the length of the filter smoothing window (odd integers only). Default is based upon the data frequency.
Inits=integer
Specify number of inner iterations. Default is 5.
Outits=integer
Specify the number of outer iterations. Default is 15.
Estsmpl=arg
Set the estimation sample.
Forclen=integer
Set the length of the forecast.
Seasdiagnostic
Display seasonality diagnostics graph.
Examples
Co2.stl co2_sa c02_trend
performs STL decomposition on the series C02, saving the adjusted data in the series C02_SA and the trend in C02_TREND.
show Co2.stl(sl=20, outits=20, seasdiagnostic)
performs the same decomposition, but with a seasonal smoothing window of 20, using 20 iterations of the outer loop, and displays the seasonal diagnostics graphs.