# Replace Missing Values

Missing observations can be problematic in analysis, and some time series measures cannot 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.
- If you create new series that contain forecasts beyond the end of the existing series (by clicking a Save button and making suitable choices), the original series and the generated residual series will have missing data for the new observations.
- Some transformations (for example, the log transformation) produce missing data for certain values of the original series.

Missing data at the beginning or end of a series
pose no particular problem; they simply shorten the useful length
of the series. Gaps in the middle of a series (*embedded* missing data) can be a much more serious problem.
The extent of the problem depends on the analytical procedure you
are using.

The Replace Missing Values dialog box allows you to
create new time series variables from existing ones, replacing missing values with estimates
computed with one of several methods. Default new variable names are
the first six characters of the existing variable used to create it,
followed by an underscore and a sequential number. For example, for
the variable *price*, the new variable
name would be *price_1*. The new
variables retain any defined value labels from the original variables.

To Replace Missing Values for Time Series Variables

- From the menus choose:
- Select the estimation method you want to use to replace missing values.
- Select the variable(s) for which you want to replace missing values.

Optionally, you can:

- Enter variable names to override the default new variable names.
- Change the estimation method for a selected variable.