Maximum Likelihood Estimation with Stata, Fourth Edition

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.

THEORY AND PRACTICE The likelihood-maximization problem Likelihood theory The maximization problemMonitoring convergence INTRODUCTION TO ml The probit model Normal linear regression Robust standard errors Weighted estimation Other features of method-gf0 evaluators Limitations OVERVIEW OF ml The terminology of ml Equations in ml Likelihood-evaluator methods Tools for the ml programmer Common ml optionsMaximizing your own likelihood functions METHOD lf The linear-form restrictions Examples The importance of generating temporary variables as doubles Problems you can safely ignore Nonlinear specifications The advantages of lf in terms of execution speed The advantages of lf in terms of accuracy METHODS lf0, lf1, AND lf2 Comparing these methods Outline of evaluators of methods lf0, lf1, and lf2Summary of methods lf0, lf1, and lf2Examples METHODS d0, d1, AND d2 Comparing these methods Outline of method d0, d1, and d2 evaluators Summary of methods d0, d1, and d2Panel-data likelihoodsOther models that do not meet the linear-form restrictions DEBUGGING LIKELIHOOD EVALUATORS ml check Using the debug methods ml trace SETTING INITIAL VALUESml search ml plot ml init INTERACTIVE MAXIMIZATION The iteration log Pressing the Break key Maximizing difficult likelihood functions FINAL RESULTS Graphing convergence Redisplaying output MATA-BASED LIKELIHOOD EVALUATORSIntroductory examples Evaluator function prototypes Utilities Random-effects linear regression WRITING DO-FILES TO MAXIMIZE LIKELIHOODSThe structure of a do-file Putting the do-file into production WRITING ADO-FILES TO MAXIMIZE LIKELIHOODSWriting estimation commands The standard estimation-command outline Outline for estimation commands using ml Using ml in noninteractive mode Advice WRITING ADO-FILES FOR SURVEY DATA ANALYSIS Program properties Writing your own predict command OTHER EXAMPLES The logit model The probit model Normal linear regression The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model APPENDIX A: Syntax of mlAPPENDIX B: Likelihood-evaluator checklistsAPPENDIX C: Listing of estimation commands ReferencesAuthor IndexSubject Index

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.