5th Edition
Maximum Likelihood Estimation with Stata, Fifth Edition
Theory and practice
The likelihood-maximization problem
Likelihood theory
The maximization problem
Estimation with mlexp
Syntax
Normal linear regression
Initial values
Restricted parameters
Robust standard errors
The probit model
Specifying derivatives
Additional estimation features
Wrapping up
Introduction to ml
The probit mode
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
Maximizing your own likelihood functions
Appendix: More about scalar parameters
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
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
Writing do-files to maximize likelihoods
The structure of a do-file
Putting the do-file into production
Writing ado-files to maximize likelihoods
Writing 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
Mata-based likelihood evaluators
Introductory examples
Evaluator function prototypes
Utilities
Random-effects linear regression
Ado-file considerations
Mata’s moptimize() function
Introductory examples
Restricting the estimation sample
Estimation preliminaries
Estimation
Results
Estimation commands
Regression redux
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
A bivariate Poisson regression model
Epilogue
Syntax of mlexp
Syntax of ml
Syntax of moptimize()
Likelihood-evaluator checklists
Method lf
Method d0
Method d1
Method d2
Method lf0
Method lf1
Method lf2
Listing of estimation commands
The logit model
The probit model
The normal model
The Weibull model
The Cox proportional hazards model
The random-effects regression model
The seemingly unrelated regression model
A bivariate Poisson regression model
References
Biography
Jeff Pitblado is Executive Director, Statistical Software at StataCorp. Pitblado has played a leading role in the development of ml: he added the ability of ml to work with survey data, and he wrote the current implementation of ml in Mata.
Brian Poi previously worked as a developer at StataCorp and wrote many popular econometric estimators in Stata. Since then, he has applied his knowledge of econometrics and statistical programming in several areas, including macroeconomic forecasting, credit analytics, and bank stress testing.
William Gould is President Emeritus of StataCorp and headed the development of Stata for over 30 years. Gould is also the architect of Mata.
