Table of Contents
Versions of Stata
Notation and Typography
1 Theory and practice
| 1.1 |
The likelihood-maximization problem |
| 1.2 |
Likelihood theory |
| 1.2.1 |
All results are asymptotic |
| 1.2.2 |
Variance estimates and hypothesis tests |
| 1.2.3 |
Likelihood-ratio tests and Wald tests |
| 1.2.4 |
The outer product of gradients variance estimator |
| 1.2.5 |
Robust variance estimates |
| 1.3 |
The maximization problem |
| 1.3.1 |
Numerical root finding |
|
Newton's method |
|
The Newton–Raphson algorithm |
| 1.3.2 |
Quasi-Newton methods |
|
The BHHH algorithm |
|
The DFP and BFGS algorithms |
| 1.3.3 |
Numerical maximization |
| 1.3.4 |
Numerical derivatives |
| 1.3.5 |
Numerical second derivatives |
| 1.4 |
Monitoring convergence |
2 Overview of ml
| 2.1 |
The jargon of ml |
| 2.2 |
Equations in ml |
| 2.3 |
Likelihood-evaluator methods |
| 2.4 |
Tools for the ml programmer |
| 2.5 |
Common ml options |
| 2.5.1 |
Subsamples |
| 2.5.2 |
Weights |
| 2.5.3 |
OPG estimates of variance |
| 2.5.4 |
Survey data |
| 2.5.6 |
Constraints |
| 2.5.7 |
Choosing among the optimization algorithms |
| 2.6 |
Maximizing your own likelihood functions |
3 Method lf
| 3.1 |
The linear-form restrictions |
| 3.2 |
Examples |
| 3.2.1 |
The probit model |
| 3.2.2 |
The normal model: linear regression |
| 3.2.3 |
The Weibull model |
| 3.3 |
The importance of generating temporary variables as doubles |
| 3.4 |
Problems you can safely ignore |
| 3.5 |
Nonlinear specifications |
| 3.6 |
The advantages of lf in terms of execution speed |
| 3.7 |
The advantages of lf in terms of accuracy |
4 Methods d0, d1, and d2
| 4.1 |
Comparing these methods |
| 4.2 |
Outline of method d0, d1, and d2 evaluators |
| 4.2.1 |
The todo argument |
| 4.2.2 |
The b argument |
|
Using mleval to obtain values from each equation |
| 4.2.3 |
The lnf argument |
|
Using lnf to indicate that the likelihood cannot be calculated |
|
Using mlsum to define lnf |
| 4.2.4 |
The g argument |
|
Using mlvecsum to define g |
|
Scores for robust and OPG variance estimates (optional) |
| 4.2.5 |
The negH argument |
|
Using mlmatsum to define negH |
| 4.2.6 |
Aside: Stata's scalars |
| 4.3 |
Summary of methods d0, d1, and d2 |
| 4.3.1 |
Method d0 |
| 4.3.2 |
Method d1 |
| 4.3.3 |
Method d2 |
| 4.4 |
Linear-form examples |
| 4.4.1 |
The probit model |
| 4.4.2 |
The normal model: linear regression |
| 4.4.3 |
The Weibull model |
| 4.5 |
Panel-data likelihoods |
| 4.5.1 |
Calculating lnf |
| 4.5.2 |
Calculating g |
| 4.5.3 |
Calculating negH |
|
Using mlmatbysum to help define negH |
| 4.6 |
Likelihoods other than linear form |
5 Debugging likelihood evaluators
| 5.1 |
ml check |
| 5.2 |
Using methods d1debug and d2debug |
| 5.2.1 |
Method d1debug |
| 5.2.2 |
Method d2debug |
| 5.3 |
ml trace |
6 Setting initial values
| 6.1 |
ml search |
| 6.2 |
ml plot |
| 6.3 |
ml init |
7 Interactive maximization
| 7.1 |
The iteration log |
| 7.2 |
Pressing the Break key |
| 7.4 |
Maximizing difficult likelihood functions |
8 Final results
| 8.1 |
Graphing convergence |
| 8.2 |
Redisplaying output |
9 Writing do-files to maximize likelihoods
| 9.1 |
The structure of a do-file |
| 9.2 |
Putting the do-file into production |
10 Writing ado-files to maximize likelihoods
| 10.1 |
Writing estimation commands |
| 10.2 |
The standard estimation-command outline |
| 10.3 |
Outline for estimation commands using ml |
| 10.4 |
Using ml in noninteractive mode |
| 10.5 |
Advice |
| 10.5.1 |
Syntax |
| 10.5.2 |
Estimation subsample |
| 10.5.3 |
Parsing with help from mlopts |
| 10.5.4 |
Weights |
| 10.5.5 |
Constant-only model |
| 10.5.6 |
Initial values |
| 10.5.7 |
Saving results in e() |
| 10.5.8 |
Displaying ancillary parameters |
| 10.5.9 |
Exponentiated coefficients |
| 10.5.10 |
Offsetting linear equations |
| 10.5.11 |
Program properties |
11 Writing ado-files for survey data analysis
| 11.1 |
Program properties |
| 11.2 |
Writing your own predict command |
12 Other examples
| 12.1 |
The logit model |
| 12.2 |
The probit model |
| 12.3 |
The normal model |
| 12.4 |
The Weibull model |
| 12.5 |
The Cox proportional hazards model |
| 12.6 |
The Cox proportional hazards model |
| 12.7 |
The seemingly unrelated regression model |
A Syntax of ml
B Likelihood evaluator checklists
| B.1 |
Method If |
| B.2 |
Method d0 |
| B.3 |
Method d1 |
| B.4 |
Method d2 |
C Listing of estimation commands
| C.1 |
The logit model |
| C.2 |
The probit model |
| C.3 |
The normal model |
| C.4 |
The Weibull model |
| C.5 |
The Cox proportional hazards model |
| C.6 |
The random-effects regression model |
| C.7 |
The seemingly unrelated regression model |
References
Author index (pdf)
Subject index (pdf)
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