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.

This edition explains how to compute maximum likelihood estimators that are not available as prepackaged routines. The book introduces the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. It then describes each of the four types of likelihood evaluator programs and provides numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. The text also includes additional details about the ml command, provides checklists to follow when writing evaluators, and shows how to write your own estimation commands.

1 Theory and practice 2 Introduction to ml 3 Overview of ml 4 Method lf 5 Methods lf0, lf1, and lf2 6 Methods d0, d1, and d2 7 Debugging likelihood evaluators 8 Setting initial values 9 Interactive maximization 10 Final results 11 Mata-based likelihood evaluators 12 Writing do-files to maximize likelihoods 13 Writing ado-files to mazximize likelihoods 14 Writing ado-files for survy data analysis 15 Other examples A Syntax of ml B Likelihood-evaluator checklists C Listing of estimation commands References Author index Subject index