Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.

About the Author

William Gould is president of StataCorp and heads the technical development of Stata. He is also the architect of Mata, Stata’s matrix programming language.

Jeff Pitblado is associate director of statistical software at StataCorp. He has played a leading role in the development of ml through adding the ability of ml to work with survey data and writing the current implementation of ml in Mata.

Brian Poi is senior economist at StataCorp. On the software development side, he has written a variety of econometric estimators in Stata.

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