Stata Press Kitaplar Maximum Likelihood Estimation with Stata, 3rd Edition William Gould, Jeffrey Pitblado, and William Sribney Copyright 2006 ISBN-10: 1-59718-012-2 ISBN-13: 978-1-59718-012-2 290 sayfa; paperback Fiyat: $65.00 (KDV ve kargo ücreti dahildir) See a larger photo of the front cover See the back cover Table of contents Preface (pdf) Author index (pdf) Subject index (pdf) Errata Download the datasets used in this book Comment from the Stata technical group Maximum Likelihood Estimation with Stata, 3rd Edition, is written for researchers in all disciplines who need to fit models using maximum likelihood estimation. This edition offers a wealth of material about the ml command, updated to include new features introduced in Stata 9. Noteworthy features in ml include constraints() — linear constraints technique() — four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH) vce(oim) — observed information matrix variance estimator vce(opg) — outer product of gradients variance estimator vce(robust) — Huber/White/sandwich/robust variance estimator svy — complete and automatic support for survey data analysis In addition, the authors give advice for developing your own estimation command and illustrate how to write your estimation command so that it supports the new svy prefix introduced in Stata 9. In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. Table of contents Preface (pdf) 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 Robust estimates of variance 2.5.5 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 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 7.3 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: linear regression 12.4 The Weibull model 12.5 The Cox proportional hazards model 12.6 The random-effects regression model 12.7 The seemingly unrelated regression model A Syntax of ml B Likelihood evaluator checklists B.1 Method lf 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)