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书籍名称:Microeconometrics Using Stata, Revised Edition
出版社:Stata Press
作者: A. Colin Cameron and Pravin K. Trivedi
出版时间:2010-04-08
语种: 英文
页数: 706
印刷日期:2010-04-08
开本: 胶版纸
纸张:706 I S B N: 978-1-59718-073-3
装订: 平装

简介

Microeconometrics Using Stata, Revised Edition, by A. Colin Cameron and Pravin K. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and up-to-date survey of microeconometric methods available in Stata. The revised edition has been updated to reflect the new features available in Stata 11 germane to microeconomists. Instead of using mfx and the community-contributed margeff commands, the revised edition uses the new margins command, emphasizing both marginal effects at the means and average marginal effects. Factor variables, which allow you to specify indicator variables and interaction effects, replace the xi command. The new gmm command for generalized method of moments and nonlinear instrumental-variables estimation is presented, along with several examples. Finally, the chapter on maximum likelihood estimation incorporates the enhancements made to ml in Stata 11. Early in the book, Cameron and Trivedi introduce simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. While simulation methods are important tools for econometricians, they are not covered in standard textbooks. By introducing simulation methods, the authors arm students and researchers with techniques they can use in future work. Cameron and Trivedi address each topic with an in-depth Stata example, and they reference their 2005 textbook, Microeconometrics: Methods and Applications, where appropriate. The authors also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. Although the book is not specifically about Stata programming, it does show how to solve many programming problems. These techniques are essential in applied microeconometrics because there will always be new, specialized methods beyond what has already been incorporated into a software package. Cameron and Trivedi’s choice of topics perfectly reflects the current practice of modern microeconometrics. After introducing the reader to Stata, the authors introduce linear regression, simulation, and generalized least-squares methods. The section on cross-sectional techniques is thorough, with up-to-date treatments of instrumental-variables methods for linear models and of quantile-regression methods. The next section of the book covers estimators for the parameters of linear panel-data models. The authors’ choice of topics is unique: after addressing the standard random-effects and fixed-effects methods, the authors also describe mixed linear models—a method used in many areas outside of econometrics. Cameron and Trivedi not only address methods for nonlinear regression models but also show how to code new nonlinear estimators in Stata. In addition to detailing nonlinear methods, which are omitted from most econometrics textbooks, this section shows researchers and students how to easily implement new nonlinear estimators. The authors next describe inference using analytical and bootstrap approximations to the distribution of test statistics. This section highlights Stata’s power to easily obtain bootstrap approximations, and it also introduces the basic elements of statistical inference. Cameron and Trivedi then include an extensive section about methods for different nonlinear models. They begin by detailing methods for binary dependent variables. This section is followed by sections about multinomial models, tobit and selection models, count-data models, and nonlinear panel-data models. Two appendices about Stata programming complete the book. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make Microeconometrics Using Stata an invaluable, hands-on addition to the library of anyone who uses microeconometric methods.

目录

    List of tables
    List of figures
    Preface to the Revised Edition (pdf)
    Preface to the First Edition (pdf)
    1 Stata basics
    1.1 Interactive use 
    1.2 Documentation
    1.2.1 Stata manuals 
    1.2.2 Additional Stata resources 
    1.2.3 The help command 
    1.2.4 The search, findit, and hsearch commands
    1.3 Command syntax and operators
    1.3.1 Basic command syntax 
    1.3.2 Example: The summarize command 
    1.3.3 Example: The regress command 
    1.3.4 Abbreviations, case sensitivity, and wildcards 
    1.3.5 Arithmetic, relational, and logical operators 
    1.3.6 Error messages
    1.4 Do-files and log files
    1.4.1 Writing a do-file 
    1.4.2 Running do-files 
    1.4.3 Log files 
    1.4.4 A three-step process 
    1.4.5 Comments and long lines 
    1.4.6 Different implementations of Stata 
    1.5 Scalars and matrices
    1.5.1 Scalars 
    1.5.2 Matrices
    1.6 Using results from Stata commands
    1.6.1 Using results from the r-class command summarize 
    1.6.2 Using results from the e-class command regress
    1.7 Global and local macros 
    1.7.1 Global macros 
    1.7.2 Local macros 
    1.7.3 Scalar or macro? 
    1.8 Looping commands 
    1.8.1 The foreach loop 
    1.8.2 The forvalues loop 
    1.8.3 The while loop 
    1.8.4 The continue command 
    1.9 Some useful commands 
    1.10 Template do-file 
    1.11 User-written commands 
    1.12 Stata resources 
    1.13 Exercises
    2 Data management and graphics
    2.1 Introduction 
    2.2 Types of data
    2.2.1 Text or ASCII data 
    2.2.2 Internal numeric data 
    2.2.3 String data 
    2.2.4 Formats for displaying numeric data
    2.3 Inputting data
    2.3.1 General principles 
    2.3.2 Inputting data already in Stata format 
    2.3.3 Inputting data from the keyboard 
    2.3.4 Inputting nontext data 
    2.3.5 Inputting text data from a spreadsheet 
    2.3.6 Inputting text data in free format 
    2.3.7 Inputting text data in fixed format 
    2.3.8 Dictionary files 
    2.3.9 Common pitfalls
    2.4 Data management 
    2.4.1 PSID example 
    2.4.2 Naming and labeling variables 
    2.4.3 Viewing data 
    2.4.4 Using original documentation 
    2.4.5 Missing values 
    2.4.6 Imputing missing data 
    2.4.7 Transforming data (generate, replace, egen, recode)
    The generate and replace commands 
    The egen command 
    The recode command 
    The by prefix 
    Indicator variables 
    Set of indicator variables 
    Interactions 
    Demeaning
    2.4.8 Saving data 
    2.4.9 Selecting the sample
    2.5 Manipulating datasets 
    2.5.1 Ordering observations and variables 
    2.5.2 Preserving and restoring a dataset 
    2.5.3 Wide and long forms for a dataset 
    2.5.4 Merging datasets 
    2.5.5 Appending datasets 
    2.6 Graphical display of data 
    2.6.1 Stata graph commands
    Example graph commands 
    Saving and exporting graphs 
    Learning how to use graph commands
    2.6.2 Box-and-whisker plot 
    2.6.3 Histogram 
    2.6.4 Kernel density plot 
    2.6.5 Twoway scatterplots and fitted lines 
    2.6.6 Lowess, kernel, local linear, and nearest-neighbor regression 
    2.6.7 Multiple scatterplots
    2.7 Stata resources 
    2.8 Exercises
    3 Linear regression basics
    3.1 Introduction 
    3.2 Data and data summary
    3.2.1 Data description 
    3.2.2 Variable description 
    3.2.3 Summary statistics 
    3.2.4 More-detailed summary statistics 
    3.2.5 Tables for data 
    3.2.6 Statistical tests 
    3.2.7 Data plots 
    3.3 Regression in levels and logs 
    3.3.1 Basic regression theory 
    3.3.2 OLS regression and matrix algebra 
    3.3.3 Properties of the OLS estimator 
    3.3.4 Heteroskedasticity-robust standard errors 
    3.3.5 Cluster–robust standard errors 
    3.3.6 Regression in logs 
    3.4 Basic regression analysis 
    3.4.1 Correlations 
    3.4.2 The regress command 
    3.4.3 Hypothesis tests 
    3.4.4 Tables of output from several regressions 
    3.4.5 Even better tables of regression output 3.4.6 Factor variables for categorical variables and interactions
    3.5 Specification analysis 
    3.5.1 Specification tests and model diagnostics 
    3.5.2 Residual diagnostic plots 
    3.5.3 Influential observations 
    3.5.4 Specification tests 
    Test of omitted variables 
    Test of the Box–Cox model 
    Test of the functional form of the conditional mean 
    Heteroskedasticity test 
    Omnibus test 
    3.5.5 Tests have power in more than one direction
    3.6 Prediction 
    3.6.1 In-sample prediction 
    3.6.2 MEs and elasticities 
    3.6.3 Prediction in logs: The retransformation problem 
    3.6.4 Prediction exercise 
    3.7 Sampling weights 
    3.7.1 Weights 
    3.7.2 Weighted mean 
    3.7.3 Weighted regression 
    3.7.4 Weighted prediction and MEs
    3.8 OLS using Mata 
    3.9 Stata resources 
    3.10 Exercises
    4 Simulation
    4.1 Introduction 
    4.2 Pseudorandom-number generators: Introduction
    4.2.1 Uniform random-number generation 
    4.2.2 Draws from normal 
    4.2.3 Draws from t, chi-squared, F, gamma, and beta 
    4.2.4 Draws from binomial, Poisson, and negative binomial 
    Independent (but not identically distributed) draws from binomial 
    Independent (but not identically distributed) draws from Poisson
    Histograms and density plots
    4.3 Distribution of the sample mean 
    4.3.1 Stata program 
    4.3.2 The simulate command 
    4.3.3 Central limit theorem simulation 
    4.3.4 The postfile command 
    4.3.5 Alternative central limit theorem simulation
    4.4 Pseudorandom-number generators: Further details 
    4.4.1 Inverse-probability transformation 
    4.4.2 Direct transformation 
    4.4.3 Other methods 
    4.4.4 Draws from truncated normal 
    4.4.5 Draws from multivariate normal 
    Direct draws from multivariate normal 
    Transformation using Cholesky decomposition
    4.4.6 Draws using Markov chain Monte Carlo method
    4.5 Computing integrals
    4.5.1 Quadrature 
    4.5.2 Monte Carlo integration
    4.5.3 Monte Carlo integration using different S
    4.6 Simulation for regression: Introduction
    4.6.1 Simulation example: OLS with X2 errors 
    4.6.2 Interpreting simulation output
    Unbiasedness of estimator 
    Standard errors 
    t statistic 
    Test size 
    Number of simulations 
    4.6.3 Variations 
    Different sample size and number of simulations 
    Test power 
    Different error distributions 
    4.6.4 Estimator inconsistency 
    4.6.5 Simulation with endogenous regressors
    4.7 Stata resources 
    4.8 Exercises 
    5 GLS regression
    5.1 Introduction 
    5.2 GLS and FGLS regression
    5.2.1 GLS for heteroskedastic errors 
    5.2.2 GLS and FGLS 
    5.2.3 Weighted least squares and robust standard errors 
    5.2.4 Leading examples 
    5.3 Modeling heteroskedastic data 
    5.3.1 Simulated dataset 
    5.3.2 OLS estimation 
    5.3.3 Detecting heteroskedasticity 
    5.3.4 FGLS estimation 
    5.3.5 WLS estimation 
    5.4 System of linear regressions 
    5.4.1 SUR model 
    5.4.2 The sureg command 
    5.4.3 Application to two categories of expenditures 
    5.4.4 Robust standard errors 
    5.4.5 Testing cross-equation constraints 
    5.4.6 Imposing cross-equation constraints 
    5.5 Survey data: Weighting, clustering, and stratification 
    5.5.1 Survey design 
    5.5.2 Survey mean estimation 
    5.5.3 Survey linear regression 
    5.6 Stata resources 
    5.7 Exercises 
    6 Linear instrumental-variables regression
    6.1 Introduction 
    6.2 IV estimation
    6.2.1 Basic IV theory 
    6.2.2 Model setup 
    6.2.3 IV estimators: IV, 2SLS, and GMM 
    6.2.4 Instrument validity and relevance 
    6.2.5 Robust standard-error estimates 
    6.3 IV example
    6.3.1 The ivregress command 
    6.3.2 Medical expenditures with one endogenous regressor 
    6.3.3 Available instruments 
    6.3.4 IV estimation of an exactly identified model 
    6.3.5 IV estimation of an overidentified model 
    6.3.6 Testing for regressor endogeneity 
    6.3.7 Tests of overidentifying restrictions 
    6.3.8 IV estimation with a binary endogenous regressor 
    6.4 Weak instruments
    6.4.1 Finite-sample properties of IV estimators 
    6.4.2 Weak instruments 
    Diagnostics for weak instruments 
    Formal tests for weak instruments
    6.4.3 The estat firststage command 
    6.4.4 Just-identified model 
    6.4.5 Overidentified model 
    6.4.6 More than one endogenous regressor 
    6.4.7 Sensitivity to choice of instruments
    6.5 Better inference with weak instruments
    6.5.1 Conditional tests and confidence intervals 
    6.5.2 LIML estimator 
    6.5.3 Jackknife IV estimator 
    6.5.4 Comparison of 2SLS, LIML, JIVE, and GMM
    6.6 3SLS systems estimation 
    6.7 Stata resources 
    6.8 Exercises
    7 Quantile regression
    7.1 Introduction 
    7.2 QR 
    7.2.1 Conditional quantiles 
    7.2.2 Computation of QR estimates and standard errors 
    7.2.3 The qreg, bsqreg, and sqreg commands
    7.3 QR for medical expenditures data 
    7.3.1 Data summary 
    7.3.2 QR estimates 
    7.3.3 Interpretation of conditional quantile coefficients 
    7.3.4 Retransformation 
    7.3.5 Comparison of estimates at different quantiles 
    7.3.6 Heteroskedasticity test 
    7.3.7 Hypothesis tests 
    7.3.8 Graphical display of coefficients over quantiles 
    7.4 QR for generated heteroskedastic data
    7.4.1 Simulated dataset 
    7.4.2 QR estimates 
    7.5 QR for count data
    7.5.1 Quantile count regression 
    7.5.2 The qcount command 
    7.5.3 Summary of doctor visits data 
    7.5.4 Results from QCR
    7.6 Stata resources 
    7.7 Exercises 
    8 Linear panel-data models: Basics
    8.1 Introduction 
    8.2 Panel-data methods overview
    8.2.1 Some basic considerations 
    8.2.2 Some basic panel models 
    Individual-effects model 
    Fixed-effects model 
    Random-effects model 
    Pooled model or population-averaged model 
    Two-way–effects model 
    Mixed linear models 
    8.2.3 Cluster–robust inference 
    8.2.4 The xtreg command 
    8.2.5 Stata linear panel-data commands
    8.3 Panel-data summary
    8.3.1 Data description and summary statistics 
    8.3.2 Panel-data organization 
    8.3.3 Panel-data description 
    8.3.4 Within and between variation 
    8.3.5 Time-series plots for each individual 
    8.3.6 Overall scatterplot 
    8.3.7 Within scatterplot 
    8.3.8 Pooled OLS regression with cluster–robust standard errors 
    8.3.9 Time-series autocorrelations for panel data 
    8.3.10 Error correlation in the RE model 
    8.4 Pooled or population-averaged estimators 
    8.4.1 Pooled OLS estimator 
    8.4.2 Pooled FGLS estimator or population-averaged estimator 
    8.4.3 The xtreg, pa command 
    8.4.4 Application of the xtreg, pa command
    8.5 Within estimator
    8.5.1 Within estimator 
    8.5.2 The xtreg, fe command 
    8.5.3 Application of the xtreg, fe command 
    8.5.4 Least-squares dummy-variables regression
    8.6 Between estimator
    8.6.1 Between estimator 
    8.6.2 Application of the xtreg, be command
    8.7 RE estimator
    8.7.1 RE estimator 
    8.7.2 The xtreg, re command 
    8.7.3 Application of the xtreg, re command
    8.8 Comparison of estimators 
    8.8.1 Estimates of variance components 
    8.8.2 Within and between R-squared 
    8.8.3 Estimator comparison 
    8.8.4 Fixed effects versus random effects 
    8.8.5 Hausman test for fixed effects
    The hausman command 
    Robust Hausman test
    8.8.6 Prediction
    8.9 First-difference estimator
    8.9.1 First-difference estimator 
    8.9.2 Strict and weak exogeneity
    8.10 Long panels
    8.10.1 Long-panel dataset 
    8.10.2 Pooled OLS and PFGLS 
    8.10.3 The xtpcse and xtgls commands 
    8.10.4 Application of the xtgls, xtpcse, and xtscc commands 
    8.10.5 Separate regressions 
    8.10.6 FE and RE models 
    8.10.7 Unit roots and cointegration 
    8.11 Panel-data management 
    8.11.1 Wide-form data 
    8.11.2 Convert wide form to long form 
    8.11.3 Convert long form to wide form 
    8.11.4 An alternative to wide-form data
    8.12 Stata resources 
    8.13 Exercises
    9 Linear panel-data models: Extensions
    9.1 Introduction 
    9.2 Panel IV estimation
    9.2.1 Panel IV 
    9.2.2 The xtivreg command 
    9.2.3 Application of the xtivreg command 
    9.2.4 Panel IV extensions 
    9.3 Hausman–Taylor estimator 
    9.3.1 Hausman–Taylor estimator 
    9.3.2 The xthtaylor command 
    9.3.3 Application of the xthtaylor command
    9.4 Arellano–Bond estimator 
    9.4.1 Dynamic model 
    9.4.2 IV estimation in the FD model 
    9.4.3 The xtabond command 
    9.4.4 Arellano–Bond estimator: Pure time series 
    9.4.5 Arellano–Bond estimator: Additional regressors 
    9.4.6 Specification tests 
    9.4.7 The xtdpdsys command 
    9.4.8 The xtdpd command
    9.5 Mixed linear models
    9.5.1 Mixed linear model 
    9.5.2 The xtmixed command 
    9.5.3 Random-intercept model 
    9.5.4 Cluster–robust standard errors 
    9.5.5 Random-slopes model 
    9.5.6 Random-coefficients model 
    9.5.7 Two-way random-effects model
    9.6 Clustered data
    9.6.1 Clustered dataset 
    9.6.2 Clustered data using nonpanel commands 
    9.6.3 Clustered data using panel commands 
    9.6.4 Hierarchical linear models
    9.7 Stata resources 
    9.8 Exercises
    10 Nonlinear regression methods
    10.1 Introduction 
    10.2 Nonlinear example: Doctor visits
    10.2.1 Data description 
    10.2.2 Poisson model description 
    10.3 Nonlinear regression methods
    10.3.1 MLE 
    10.3.2 The poisson command 
    10.3.3 Postestimation commands 
    10.3.4 NLS 
    10.3.5 The nl command 
    10.3.6 GLM 
    10.3.7 The glm command 
    10.3.8 The gmm command 
    10.3.9 Other estimators
    10.4 Different estimates of the VCE
    10.4.1 General framework 
    10.4.2 The vce() option 
    10.4.3 Application of the vce() option 
    10.4.4 Default estimate of the VCE 
    10.4.5 Robust estimate of the VCE 
    10.4.6 Cluster–robust estimate of the VCE 
    10.4.7 Heteroskedasticity- and autocorrelation-consistent estimate of the VCE 
    10.4.8 Bootstrap standard errors 
    10.4.9 Statistical inference
    10.5 Prediction
    10.5.1 The predict and predictnl commands 
    10.5.2 Application of predict and predictnl 
    10.5.3 Out-of-sample prediction 
    10.5.4 Prediction at a specified value of one of the regressors 
    10.5.5 Prediction at a specified value of all the regressors 
    10.5.6 Prediction of other quantities 
    10.5.7 The margins command for prediction 
    10.6 Marginal effects
    10.6.1 Calculus and finite-difference methods 
    10.6.2 MEs estimates AME, MEM, and MER 
    10.6.3 Elasticities and semielasticities 
    10.6.4 Simple interpretations of coefficients in single-index models 
    10.6.5 The margins command for marginal effects 
    10.6.6 MEM: Marginal effect at mean
    Comparison of calculus and finite-difference methods
    10.6.7 MER: Marginal effect at representative value 
    10.6.8 AME: Average marginal effect 
    10.6.9 Elasticities and semielasticities 
    10.6.10 AME computed manually 
    10.6.11 Polynomial regressors 
    10.6.12 Interacted regressors 
    10.6.13 Complex interactions and nonlinearities 
    10.7 Model diagnostics
    10.7.1 Goodness-of-fit measures 
    10.7.2 Information criteria for model comparison 
    10.7.3 Residuals 
    10.7.4 Model-specification tests
    10.8 Stata resources 
    10.9 Exercises
    11 Nonlinear optimization methods
    11.1 Introduction 
    11.2 Newton–Raphson method
    11.2.1 NR method 
    11.2.2 NR method for Poisson 
    11.2.3 Poisson NR example using Mata
    Core Mata code for Poisson NR iterations 
    Complete Stata and Mata code for Poisson NR iterations
    11.3 Gradient methods
    11.3.1 Maximization options 
    11.3.2 Gradient methods 
    11.3.3 Messages during iterations 
    11.3.4 Stopping criteria 
    11.3.5 Multiple maximums 
    11.3.6 Numerical derivatives
    11.4 The ml command: lf method
    11.4.1 The ml command 
    11.4.2 The lf method 
    11.4.3 Poisson example: Single-index model 
    11.4.4 Negative binomial example: Two-index model 
    11.4.5 NLS example: Nonlikelihood model
    11.5 Checking the program
    11.5.1 Program debugging using ml check and ml trace 
    11.5.2 Getting the program to run 
    11.5.3 Checking the data 
    11.5.4 Multicollinearity and near collinearity 
    11.5.5 Multiple optimums 
    11.5.6 Checking parameter estimation 
    11.5.7 Checking standard-error estimation 
    11.6 The ml command: d0, d1, d2, lf0, lf1, and lf2 methods
    11.6.1 Evaluator functions 
    11.6.2 The d0 method 
    11.6.3 The d1 method 
    11.6.4 The lf1 method with the robust estimate of the VCE 
    11.6.5 The d2 and lf2 methods
    11.7 The Mata optimize() function
    11.7.1 Type d and gf evaluators 
    11.7.2 Optimize functions 
    11.7.3 Poisson example
    Evaluator program for Poisson MLE 
    The optimize() function for Poisson MLE
    11.8 Generalized method of moments
    11.8.1 Definition 
    11.8.2 Nonlinear IV example 
    11.8.3 GMM using the Mata optimize() function
    11.9 Stata resources 
    11.10 Exercises
    12 Testing methods
    12.1 Introduction 
    12.2 Critical values and p-values
    12.2.1 Standard normal compared with Student's t 
    12.2.2 Chi-squared compared with F 
    12.2.3 Plotting densities 
    12.2.4 Computing p-values and critical values 
    12.2.5 Which distributions does Stata use?
    12.3 Wald tests and confidence intervals
    12.3.1 Wald test of linear hypotheses 
    12.3.2 The test command
    Test single coefficient 
    Test several hypotheses 
    Test of overall significance 
    Test calculated from retrieved coefficients and VCE
    12.3.3 One-sided Wald tests 
    12.3.4 Wald test of nonlinear hypotheses (delta method) 
    12.3.5 The testnl command 
    12.3.6 Wald confidence intervals 
    12.3.7 The lincom command 
    12.3.8 The nlcom command (delta method) 
    12.3.9 Asymmetric confidence intervals
    12.4 Likelihood-ratio tests
    12.4.1 Likelihood-ratio tests 
    12.4.2 The lrtest command 
    12.4.3 Direct computation of LR tests 
    12.5 Lagrange multiplier test (or score test)
    12.5.1 LM tests 
    12.5.2 The estat command 
    12.5.3 LM test by auxiliary regression
    12.6 Test size and power
    12.6.1 Simulation DGP: OLS with chi-squared errors 
    12.6.2 Test size 
    12.6.3 Test power 
    12.6.4 Asymptotic test power 
    12.7 Specification tests
    12.7.1 Moment-based tests 
    12.7.2 Information matrix test 
    12.7.3 Chi-squared goodness-of-fit test 
    12.7.4 Overidentifying restrictions test 
    12.7.5 Hausman test 
    12.7.6 Other tests
    12.8 Stata resources 
    12.9 Exercises
    13 Bootstrap methods
    13.1 Introduction 
    13.2 Bootstrap methods 
    13.2.1 Bootstrap estimate of standard error 
    13.2.2 Bootstrap methods 
    13.2.3 Asymptotic refinement 
    13.2.4 Use the bootstrap with caution 
    13.3 Bootstrap pairs using the vce(bootstrap) option
    13.3.1 Bootstrap-pairs method to estimate VCE 
    13.3.2 The vce(bootstrap) option 
    13.3.3 Bootstrap standard-errors example 
    13.3.4 How many bootstraps? 
    13.3.5 Clustered bootstraps 
    13.3.6 Bootstrap confidence intervals 
    13.3.7 The postestimation estat bootstrap command 
    13.3.8 Bootstrap confidence-intervals example 
    13.3.9 Bootstrap estimate of bias
    13.4 Bootstrap pairs using the bootstrap command 
    13.4.1 The bootstrap command 
    13.4.2 Bootstrap parameter estimate from a Stata estimation command 
    13.4.3 Bootstrap standard error from a Stata estimation command 
    13.4.4 Bootstrap standard error from a user-written estimation command 
    13.4.5 Bootstrap two-step estimator 
    13.4.6 Bootstrap Hausman test 
    13.4.7 Bootstrap standard error of the coefficient of variation
    13.5 Bootstraps with asymptotic refinement
    13.5.1 Percentile-t method 
    13.5.2 Percentile-t Wald test 
    13.5.3 Percentile-t Wald confidence interval 
    13.6 Bootstrap pairs using bsample and simulate 
    13.6.1 The bsample command 
    13.6.2 The bsample command with simulate 
    13.6.3 Bootstrap Monte Carlo exercise
    13.7 Alternative resampling schemes 
    13.7.1 Bootstrap pairs 
    13.7.2 Parametric bootstrap 
    13.7.3 Residual bootstrap 
    13.7.4 Wild bootstrap 
    13.7.5 Subsampling 
    13.8 The jackknife
    13.8.1 Jackknife method 
    13.8.2 The vce(jackknife) option and the jackknife command
    13.9 Stata resources 
    13.10 Exercises
    14 Binary outcome models
    14.1 Introduction 
    14.2 Some parametric models
    14.2.1 Basic model 
    14.2.2 Logit, probit, linear probability, and clog-log models
    14.3 Estimation
    14.3.1 Latent-variable interpretation and identification 
    14.3.2 ML estimation 
    14.3.3 The logit and probit commands 
    14.3.4 Robust estimate of the VCE 
    14.3.5 OLS estimation of LPM
    14.4 Example
    14.4.1 Data description 
    14.4.2 Logit regression 
    14.4.3 Comparison of binary models and parameter estimates 
    14.5 Hypothesis and specification tests
    14.5.1 Wald tests 
    14.5.2 Likelihood-ratio tests 
    14.5.3 Additional model-specification tests 
    Lagrange multiplier test of generalized logit 
    Heteroskedastic probit regression 
    14.5.4 Model comparison 
    14.6 Goodness of fit and prediction 
    14.6.1 Pseudo-R2 measure 
    14.6.2 Comparing predicted probabilities with sample frequencies 
    14.6.3 Comparing predicted outcomes with actual outcomes 
    14.6.4 The predict command for fitted probabilities 
    14.6.5 The prvalue command for fitted probabilities
    14.7 Marginal effects 
    14.7.1 Marginal effect at a representative value (MER) 
    14.7.2 Marginal effect at the mean (MEM) 
    14.7.3 Average marginal effect (AME) 
    14.7.4 The prchange command
    14.8 Endogenous regressors 
    14.8.1 Example 
    14.8.2 Model assumptions 
    14.8.3 Structural-model approach
    The ivprobit command 
    Maximum likelihood estimates 
    Two-step sequential estimates 
    14.8.4 IVs approach
    14.9 Grouped data
    14.9.1 Estimation with aggregate data 
    14.9.2 Grouped-data application
    14.10 Stata resources 
    14.11 Exercises
    15 Multinomial models
    15.1 Introduction 
    15.2 Multinomial models overview
    15.2.1 Probabilities and MEs 
    15.2.2 Maximum likelihood estimation 
    15.2.3 Case-specific and alternative-specific regressors 
    15.2.4 Additive random-utility model 
    15.2.5 Stata multinomial model commands
    15.3 Multinomial example: Choice of fishing mode
    15.3.1 Data description 
    15.3.2 Case-specific regressors 
    15.3.3 Alternative-specific regressors
    15.4 Multinomial logit model
    15.4.1 The mlogit command 
    15.4.2 Application of the mlogit command 
    15.4.3 Coefficient interpretation 
    15.4.4 Predicted probabilities 
    15.4.5 MEs
    15.5 Conditional logit model
    15.5.1 Creating long-form data from wide-form data 
    15.5.2 The asclogit command 
    15.5.3 The clogit command 
    15.5.4 Application of the asclogit command 
    15.5.5 Relationship to multinomial logit model 
    15.5.6 Coefficient interpretation 
    15.5.7 Predicted probabilities 
    15.5.8 MEs
    15.6 Nested logit model
    15.6.1 Relaxing the independence of irrelevant alternatives assumption 
    15.6.2 NL model 
    15.6.3 The nlogit command 
    15.6.4 Model estimates 
    15.6.5 Predicted probabilities 
    15.6.6 MEs 
    15.6.7 Comparison of logit models 
    15.7 Multinomial probit model
    15.7.1 MNP 
    15.7.2 The mprobit command 
    15.7.3 Maximum simulated likelihood 
    15.7.4 The asmprobit command 
    15.7.5 Application of the asmprobit command 
    15.7.6 Predicted probabilities and MEs 
    15.8 Random-parameters logit
    15.8.1 Random-parameters logit 
    15.8.2 The mixlogit command 
    15.8.3 Data preparation for mixlogit 
    15.8.4 Application of the mixlogit command 
    15.9 Ordered outcome models
    15.9.1 Data summary 
    15.9.2 Ordered outcomes 
    15.9.3 Application of the ologit command 
    15.9.4 Predicted probabilities 
    15.9.5 MEs 
    15.9.6 Other ordered models 
    15.10 Multivariate outcomes 
    15.10.1 Bivariate probit 
    15.10.2 Nonlinear SUR 
    15.11 Stata resources 
    15.12 Exercises 
    16 Tobit and selection models
    16.1 Introduction 
    16.2 Tobit model 
    16.2.1 Regression with censored data 
    16.2.2 Tobit model setup 
    16.2.3 Unknown censoring point 
    16.2.4 Tobit estimation 
    16.2.5 ML estimation in Stata
    16.3 Tobit model example 
    16.3.1 Data summary 
    16.3.2 Tobit analysis 
    16.3.3 Prediction after tobit 
    16.3.4 Marginal effects
    Left-truncated, left-censored, and right-truncated examples 
    Left-censored case computed directly 
    Marginal impact on probabilities 
    16.3.5 The ivtobit command 
    16.3.6 Additional commands for censored regression
    16.4 Tobit for lognormal data
    16.4.1 Data example 
    16.4.2 Setting the censoring point for data in logs 
    16.4.3 Results 
    16.4.4 Two-limit tobit 
    16.4.5 Model diagnostics 
    16.4.6 Tests of normality and homoskedasticity
    Generalized residuals and scores 
    Test of normality 
    Test of homoskedasticity
    16.4.7 Next step?
    16.5 Two-part model in logs 
    16.5.1 Model structure 
    16.5.2 Part 1 specification 
    16.5.3 Part 2 of the two-part model
    16.6 Selection model 
    16.6.1 Model structure and assumptions 
    16.6.2 ML estimation of the sample-selection model 
    16.6.3 Estimation without exclusion restrictions 
    16.6.4 Two-step estimation 
    16.6.5 Estimation with exclusion restrictions 
    16.7 Prediction from models with outcome in logs 
    16.7.1 Predictions from tobit 
    16.7.2 Predictions from two-part model 
    16.7.3 Predictions from selection model 
    16.8 Stata resources 
    16.9 Exercises
    17 Count-data models
    17.1 Introduction 
    17.2 Features of count data 
    17.2.1 Generated Poisson data 
    17.2.2 Overdispersion and negative binomial data 
    17.2.3 Modeling strategies 
    17.2.4 Estimation methods 
    17.3 Empirical example 1
    17.3.1 Data summary 
    17.3.2 Poisson model
    Poisson model results 
    Robust estimate of VCE for Poisson MLE 
    Test of overdispersion 
    Coefficient interpretation and marginal effects
    17.3.3 NB2 model
    NB2 model results 
    Fitted probabilities for Poisson and NB2 models 
    The countfit command 
    The prvalue command 
    Discussion 
    Generalized NB model
    17.3.4 Nonlinear least-squares estimation 
    17.3.5 Hurdle model
    Variants of the hurdle model 
    Application of the hurdle model
    17.3.6 Finite-mixture models
    FMM specification 
    Simulated FMM sample with comparisons 
    ML estimation of the FMM 
    The fmm command 
    Application: Poisson finite-mixture model 
    Interpretation 
    Comparing marginal effects 
    Application: NB finite-mixture model 
    Model selection 
    Cautionary note
    17.4 Empirical example 2 
    17.4.1 Zero-inflated data 
    17.4.2 Models for zero-inflated data 
    17.4.3 Results for the NB2 model 
    The prcounts command
    17.4.4 Results for ZINB 
    17.4.5 Model comparison 
    The countfit command 
    Model comparison using countfit 
    17.5 Models with endogenous regressors 
    17.5.1 Structural-model approach
    Model and assumptions 
    Two-step estimation 
    Application 
    17.5.2 Nonlinear IV method
    17.6 Stata resources 
    17.7 Exercises
    18 Nonlinear panel models
    18.1 Introduction 
    18.2 Nonlinear panel-data overview
    18.2.1 Some basic nonlinear panel models
    FE models 
    RE models 
    Pooled models or population-averaged models 
    Comparison of models 
    18.2.2 Dynamic models 
    18.2.3 Stata nonlinear panel commands 
    18.3 Nonlinear panel-data example
    18.3.1 Data description and summary statistics 
    18.3.2 Panel-data organization 
    18.3.3 Within and between variation 
    18.3.4 FE or RE model for these data?
    18.4 Binary outcome models
    18.4.1 Panel summary of the dependent variable 
    18.4.2 Pooled logit estimator 
    18.4.3 The xtlogit command 
    18.4.4 The xtgee command 
    18.4.5 PA logit estimator 
    18.4.6 RE logit estimator 
    18.4.7 FE logit estimator 
    18.4.8 Panel logit estimator comparison 
    18.4.9 Prediction and marginal effects 
    18.4.10 Mixed-effects logit estimator
    18.5 Tobit model 
    18.5.1 Panel summary of the dependent variable 
    18.5.2 RE tobit model 
    18.5.3 Generalized tobit models 
    18.5.4 Parametric nonlinear panel models 
    18.6 Count-data models
    18.6.1 The xtpoisson command 
    18.6.2 Panel summary of the dependent variable 
    18.6.3 Pooled Poisson estimator 
    18.6.4 PA Poisson estimator 
    18.6.5 RE Poisson estimators 
    18.6.6 FE Poisson estimator 
    18.6.7 Panel Poisson estimators comparison 
    18.6.8 Negative binomial estimators 
    18.7 Stata resources 
    18.8 Exercises
    A Programming in Stata
    A.1 Stata matrix commands
    A.1.1 Stata matrix overview 
    A.1.2 Stata matrix input and output
    Matrix input by hand 
    Matrix input from Stata estimation results 
    A.1.3 Stata matrix subscripts and combining matrices 
    A.1.4 Matrix operators 
    A.1.5 Matrix functions 
    A.1.6 Matrix accumulation commands 
    A.1.7 OLS using Stata matrix commands 
    A.2 Programs
    A.2.1 Simple programs (no arguments or access to results) 
    A.2.2 Modifying a program 
    A.2.3 Programs with positional arguments 
    A.2.4 Temporary variables 
    A.2.5 Programs with named positional arguments 
    A.2.6 Storing and retrieving program results 
    A.2.7 Programs with arguments using standard Stata syntax 
    A.2.8 Ado-files
    A.3 Program debugging
    A.3.1 Some simple tips 
    A.3.2 Error messages and return code 
    A.3.3 Trace
    B Mata
    B.1 How to run Mata
    B.1.1 Mata commands in Mata 
    B.1.2 Mata commands in Stata 
    B.1.3 Stata commands in Mata 
    B.1.4 Interactive versus batch use 
    B.1.5 Mata help
    B.2 Mata matrix commands 
    B.2.1 Mata matrix input
    Matrix input by hand 
    Identity matrices, unit vectors, and matrices of constants 
    Matrix input from Stata data 
    Matrix input from Stata matrix 
    Stata interface functions
    B.2.2 Mata matrix operators
    Element-by-element operators
    B.2.3 Mata functions
    Scalar and matrix functions 
    Matrix inversion 
    B.2.4 Mata cross products 
    B.2.5 Mata matrix subscripts and combining matrices 
    B.2.6 Transferring Mata data and matrices to Stata
    Creating Stata matrices from Mata matrices 
    Creating Stata data from a Mata vector
    B.3 Programming in Mata
    B.3.1 Declarations 
    B.3.2 Mata program 
    B.3.3 Mata program with results output to Stata 
    B.3.4 Stata program that calls a Mata program 
    B.3.5 Using Mata in ado-files 
    Glossary of abbreviations
    References
    Author index (pdf)
    Subject index (pdf)