What have you found to be the best way to determine seasonal patterns in data?
I think one statistally right way would be a seasonal Unit-Root-Test:
Proc Arima Data=<Data_Set>;
Identify Var=<Variable> Stationarity=(ADF=(1) DLag=12); /* Augmented Dickey-Fuller-Test with 1 autoregressive term; monthly data */
Identify Var=<Variable> (1) Stationarity=(ADF=(1) DLag=12); /* -"- in 1st differences */
Run;
There are other tests as well (and simpler methods). For example if you have quaterly data and carry out a trend regression you might check the dummy-coefficients for statistical significance.
I think we can't generalize which is the best way but as an initial point, data on a graph can give information about seasonal/cyclic patterns.
I think one statistally right way would be a seasonal Unit-Root-Test:
Proc Arima Data=<Data_Set>;
Identify Var=<Variable> Stationarity=(ADF=(1) DLag=12); /* Augmented Dickey-Fuller-Test with 1 autoregressive term; monthly data */
Identify Var=<Variable> (1) Stationarity=(ADF=(1) DLag=12); /* -"- in 1st differences */
Run;
There are other tests as well (and simpler methods). For example if you have quaterly data and carry out a trend regression you might check the dummy-coefficients for statistical significance.
P.S.: I guess you are aware that your question is a tricky one. If you ask your question this way, it is almost certain that the respondent puts his/her statistical reputation on the line. 🙂
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