NetCourse^TM 152: Advanced Stata Programming

Course content

Lecture 1: Parsing Stata syntax/Stata programming basics

• Review of Stata programming features you learned in NC151
• Parsing Stata syntax
• Parsing options
• Parsing complicated syntax
• Using subprograms

Lecture 2: Parsing Stata syntax, continued/Quotes, returned results, and subsamples

• Compound quotes for handling strings that may themselves contain quotes
• Temporary variables
• Using returned results from other programs
• Restricting a calculation to a subsample
• Putting together a complete program

Lecture 3: Using scalars and macros and introduction to low-level parsing

• Scalars
• Binary accuracy
• Accuracy of macros versus scalars
• Converting a program from macros to scalars
• Handling by() options
• Sorting
• Low-level parsing
• Programming immediate commands
• Parsing new variables

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There is a week break between lectures 3 and 4 to allow more time for those who may fall behind and to allow for more discussion from the participants.

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Lecture 4: Returning results and writing estimation commands

• Saved results
• What can be returned in r()?
• Referring to returned results in other programs
• Referring to returned results in the program that sets them
• Other types of returned values: s() and e()
• S-class returned values
• E-class returned results
• Writing postestimation commands
• Writing an estimation (e-class) command
• Writing estimation commands from first principles
• Writing estimation commands via maximum likelihood

Lecture 5: List processing, controlling program output, and naming conventions

• Restricting commands to the relevant subsample
• Which is better: marksample or mark?
• Programming by varlist
• Lists
• Stepping through list elements one by one
• Deleting elements from lists
• Adding elements to lists
• Macro vectors
• Parsing revisited: gettoken
• Quietly blocks
• The relation between capture and quietly
• capture blocks
• Naming conventions
• Program naming convention
• Calling convention
• Version control