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NetCourseTM 200: Maximum likelihood estimation with Stata

Estimation of user-defined models via maximum likelihood. Includes overview of theory, explanation of how optimizers work, and practical coding considerations.
Course leaders:
William Gould, President of StataCorp and Head of Development.
Course length:
7 weeks (5 lectures)
Stata 7, installed and working.
Course content of NetCourse 151 or equivalent knowledge.
Editor or word processor with which you are familiar and that can save and edit plain text files; if you have Stata for Windows or Stata for Mac, these include an editor.
(Course is platform independent.)

This syllabus is for the Stata 5.0 NetCourse 200 — it will be rewritten when we offer the course for Stata 7.0.


Lecture 1: Background on maximum likelihood estimators

Note:   Lecture 1 does not concern Stata per se. This lecture focuses on the rationale of maximum likelihood estimation and the numerical issues involved. Emphasis is on practice and providing the background to knowledgeably discuss maximum likelihood estimates and variance estimates, and how to diagnose problems.

Lectures 2 to 5 concern estimation with Stata.

Lecture 2: Introduction to Stata's ml commands and how to program the linear-form (lf) method
There is an additional one-week break between Lectures 2 and 3 in this course to allow extra time for discussion.
Lecture 3: Maximization of general likelihood functions without analytic derivatives (the deriv0 method)

Lecture 4: Maximization with analytic derivatives (the deriv1 and deriv2 methods)

Lecture 5: Robust variance estimator
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