variable selection

[abcd123]

Hello, all

I am fitting a time series model(ARIMA) and I have about 100 variables to choose from. I have tried PROC VARCLUS to reduce this number to 40, but still it is too much. Is there a way I can further reduce this number?

I know PROC GLMSELECT  could be one option; however, from what I know, PROC GLMSELECT  only works for general linear model. It it suitable to use PROC GLMSELECT for time series model selection?

Thank you very much.

[abcd123]

Hello, all

I am fitting a time series model(ARIMA) and I have about 100 variables to choose from. I have tried PROC VARCLUS to reduce this number to 40, but still it is too much. Is there a way I can further reduce this number?

I know PROC GLMSELECT  could be one option; however, from what I know, PROC GLMSELECT  only works for general linear model. It it suitable to use PROC GLMSELECT for time series model selection?

Thank you very much.

[YueLi]

You can still use GLMSELECT for variable selection in time series. However, since it is time series data, you will need to include some time series related variables in the model to better capture the time series aspects of the data, e.g. seasonality, lags of dependent variables, differencing, seasonal differencing, etc.

[abcd123]

Thanks. Are you suggesting adding those additional variables and treat the problem as a general linear model? (BTW, what does "seasonality" mean? Is it a dummy variable?)

[alexchien]

Yes the suggestion was to formulate a GLM model with time series features such as lagged dependent variable and seasonal dummies (you can use the CLASS statement in GLM to create the dummies for you automatically). The model is not exactly the same as ARIMA but you can use this proxy model to select input variables, and then use the selected input variables in the subsequent time series models.

thanks