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Working with Time Series Data

Interpolating Missing Values

To use the EXPAND procedure to interpolate missing values in a time series, specify the input and output data sets in the PROC EXPAND statement, and specify the time ID variable in an ID statement. For example, the following statements cause PROC EXPAND to interpolate values for missing values of all numeric variables in the data set USPRICE:

   proc expand data=usprice out=interpl;
      id date;

Interpolated values are computed only for embedded missing values in the input time series. Missing values before or after the range of a series are ignored by the EXPAND procedure.

In the preceding example, PROC EXPAND assumes that all series are measured at points in time given by the value of the ID variable. In fact, the series in the USPRICE data set are monthly averages. PROC EXPAND can produce a better interpolation if this is taken into account. The following example uses the FROM=MONTH option to tell PROC EXPAND that the series is monthly and uses the CONVERT statement with the OBSERVED=AVERAGE to specify that the series values are averages over each month:

   proc expand data=usprice out=interpl
      id date;
      convert cpi ppi / observed=average;
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