# Maximum Likelihood Estimation with Stata, Fourth Edition

352 pages

Paperback: 9781597180788
pub: 2010-10-27
US Dollars\$79.95
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Maximum Likelihood Estimation with Stata, Fourth Editionis 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 problem

Monitoring 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 options

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 lf2

Summary of methods lf0, lf1, and lf2

Examples

METHODS d0, d1, AND d2

Comparing these methods

Outline of method d0, d1, and d2 evaluators

Summary of methods d0, d1, and d2

Panel-data likelihoods

Other models that do not meet the linear-form restrictions

DEBUGGING LIKELIHOOD EVALUATORS

ml check

Using the debug methods

ml trace

SETTING INITIAL VALUES

ml 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 EVALUATORS

Introductory examples

Evaluator function prototypes

Utilities

Random-effects linear regression

WRITING DO-FILES TO MAXIMIZE LIKELIHOODS

The structure of a do-file

Putting the do-file into production

Writing estimation commands

The standard estimation-command outline

Outline for estimation commands using ml

Using ml in noninteractive mode

WRITING ADO-FILES FOR SURVEY DATA ANALYSIS

Program properties

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 ml

APPENDIX B: Likelihood-evaluator checklists

APPENDIX C: Listing of estimation commands

References

Author Index

Subject 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.