Maximum Likelihood Estimation with Stata  (3nd edition) by William Gould, Jeffrey Pitblado, William Sribney, (2006) Publisher: Stata Press ISBN:1-59718-012-2 Pages: 290 pages Price: £30.00 + p&p ml3.zip PKZIP format, 57K ml3.tar.Z Unix tar.Z format 51K ORDER NOW

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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