If I have missing values in a time series that has 40 quarters (ten cycles or ten years) of data, what is the best SAS procedure to use to impute the missing values?
Part 2: I have 390 series (40 quarters each) that follow similar patterns — most have missing data points (2-3 each), how do I make use of the other 390 series to help impute missing values in any one series? What SAS procedure would I use for that? In the end I want a complete set of 15600 data points.
Best Answer
For each of your 390 series you have 40 readings. Simply automatically identify an ARIMA Model for each series enabling Intervention Detection to provide estimates of the missing values. If there are 3 missing values the software will identify three Pulses which will yield an estimate if the missing values. The problem is that SAS assumes an ARIMA MODEL first and then estimates the Pulses. In truth the identified ARIMA Model may be flawed by the missing values. An alternative procedure which we have used is to identify the missing values first and then iterate to the ARIMA Model. The whole idea is to run BOTH PROCEDURES to determine which one is optimal for each of your 390 series.