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NetCourse® 152: Advanced Stata Programming

$175.00 per person

For enrollments of 5 or more participants, please contact us.

Course length: 7 weeks (5 lectures)
Dates: October 10–November 28, 2014
See alternate dates.

Course leaders:
Kerry Kammire, Technical Services Analyst at StataCorp
Derek Wagner, Technical Services Analyst at StataCorp

Content:
Learn how to create and debug your own commands that are indistinguishable from the commands in Stata. You will be able to parse both standard and nonstandard Stata syntax using the intuitive syntax command, to manage and process saved results, to post your own saved results, to process by-groups, to create data management commands, to program your own maximum-likelihood estimator, and more. In short, learn to create commands that act just like the commands that ship with Stata.

Prerequisites:

  • Stata 13, installed and working
  • Course content of NetCourse 151 or equivalent knowledge
  • Internet web browser, installed and working
    (course is platform independent)

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Course content

Lecture 1: Parsing Stata syntax/Stata programming basics

  • Review of Stata’s programming features
  • Parsing
  • Parsing options
  • Parsing complicated syntax
  • Aside on subprograms

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

  • Quotes
  • Development continues
  • Temporary variables
  • Development continues
  • An aside concerning r()
  • Programming the formulas
  • Putting it together

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

  • What you must learn
  • 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
  • Rewriting mytt in terms of mytti
  • Parsing new variables

Note: 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.

 

Lecture 4: Returning results and writing estimation commands

  • Where are we?
  • Stored 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
  • An alternative estimation command outline
  • 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
  • Creating 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
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