User Guide

171
Data Transform
ations
Running median. Median of a span of series values surrounding and including the
current value. The span is the number of series values used to compute the median.
If the span i
s even, the median is computed by averaging each pair of uncentered
medians. The number of cases with the system-missing value at the beginning and
at the end of the series for a span of n is equal to n/2 for even span values and
for odd spa
nvalues. Forexample,ifthespanis5,thenumberofcaseswiththe
system-missing value at the beginning and at the end of the series is 2.
Cumulative sum. Cumulative sum of series values up to and including the current value.
Lag. Val ue
of a previous case, based on the specified lag order. The order is the
number of cases prior to the current case from which the value is obtained. The
number of cases with the system-missing value at the beginning of the series is
equal to t
he order value.
Lead. Value of a subsequent case, based on the specified lead order. The order is
the number of cases after the current case from which the value is obtained. The
number of
cases with the system-missing value at the end of the series is equal to the
order value.
Smoothing. New series values based on a compound data smoother. The smoother
starts w
ith a running median of 4, which is centered by a running median of 2. It then
resmoothesthesevaluesbyapplyingarunningmedianof5,arunningmedianof3,
and hanning (running weighted averages). Residuals are computed by subtracting the
smoothe
d series from the original series. This whole process is then repeated on the
computed residuals. Finally, the smoothed residuals are computed by subtracting
the smoothed values obtained the first time through the process. This is sometimes
referr
ed to as T4253H smoothing.
Replace Missing Values
Missing observations can be problematic in analysis, and some time series measures
canno
t be computed if there are missing values in the series. Sometimes the value for
a particular observation is simply not known. In addition, missing data can result
from any of the following:
 Each degree of differencing reduces the length of a series by 1.
 Each degree of seasonal differencing reduces the length of a series by one season.