Software-specific Data Recommendations

SAS

For large data sets, use a Length statement to reduce the size of variables.
If you have long character strings, consider leaving them out or using a FORMAT to convert between strings and shorter codes.
For PROC GLM, if you have categorical variables with large numbers of levels, use ABSORB statement when appropriate.
In PROC MIXED, speed can depend on how the model is specified. For example, using RANDOM INTERCEPT/SUBJECT=xxx can be faster than RANDOM xxx.
For large multilevel models in PROC MIXED, consider using specialized software such as MLWin or HLM instead.
Determine whether you need all of your variables in the working dataset. Space and computing time may be saved by retaining long character strings or extraneous variables in a separate dataset. Non-essential variables can be merged back into the main datasets when needed.

Use built-in commands rather than commands implemented in ado-files if a built-in command is available with the appropriate functionality.
On the research grid, use stata-large or stata-xl only when you need more memory than the standard stata wrapper will provide you.
If you are getting unneeded output (e.g. with "by" group processing), use "quietly".
Avoid macro variable loops if possible - substitute vector-oriented data set processing.

Use sparse matrices where applicable.
Use the profiler to identify sections of code that are using the most execution time and optimize those.
Use vector and matrix operations rather than loops.

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