波动数据的时间序列建模

Question

I'm trying to model 10 years of monthly time series data that is very choppy and overal it has an upward trend. At first glance it looks like a strong seasonal series, however the test results indicate that it is definitely not seasonal. This is a pricing variable that I'm trying to model as a function of macroeconomic environment, such as interest rates and yield curves. I've tryed linear OLS regression (proc reg), but I don't get a very goo dmodel with that. I've also tried autoregressive error models (proc autoreg), but it captures 7 lags of the error term as significant factors. I don't really want to include that many lag of the error term in the model. In addition most of the macroeconomic variables become insignificant when I include all these error lags in the model.

Any suggestions on modeling method/technique that could help me model this choppy data is really appreciated.

Answer 1

At a past project, we've used proc arima to predict future product sales based on a time series of past sales: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_arima_sect019.htm (note that arima is also an autoregressive model)

But as Joe said, for really statistical feedback on your question, you're better of asking at the Cross Validated site.