Cameron A.C., Trivedi P.K. Microeconometrics Using Stata

Stata Press, 2009. — 732 p. — ISBN: 1597180483, 9781597180481.An outstanding introduction to microeconometrics and how to do microeconometric research using Stata, this book covers topics often 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. They begin by introducing simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. They address each topic with an in-depth Stata example and demonstrate how to use Stata’s programming features to implement methods for which Stata does not have a specific command.List of tables
List of figuresStata basics
Interactive use
Documentation
Command syntax and operators
Do-files and log files
Scalars and matrices
Using results from Stata commands
Global and local macros
Looping commands
Some useful commands
Template do-file
User-written commands
Stata resources
Exercises
Data management and graphicsTypes of data
Inputting data
Data management
The generate and replace commands
The egen command
The recode command
The by prefix
Indicator variables
Set of indicator variables
Interactions
Demeaning
Manipulating datasets
Graphical display of data
Example graph commands
Saving and exporting graphs
Learning how to use graph commands
Stata resources
Exercises
Linear regression basicsData and data summary
Regression in levels and logs
Basic regression analysis
Specification analysis
Test of omitted variables
Test of the Box–Cox model
Test of the functional form of the conditional mean
Heteroskedasticity test
Omnibus test
Prediction
Sampling weights
OLS using Mata
Stata resources
Exercises
SimulationPseudorandom-number generators: Introduction
Independent (but not identically distributed) draws from binomial
Independent (but not identically distributed) draws from Poisson
Histograms and density plots
Distribution of the sample mean
Pseudorandom-number generators: Further details
Direct draws from multivariate normal
Transformation using Cholesky decomposition
Computing integrals
Simulation for regression: Introduction
Unbiasedness of estimator
Standard errors
t statistic
Test size
Number of simulations
Different sample size and number of simulations
Test power
Different error distributions
Stata resources
Exercises
GLS regressionGLS and FGLS regression
Modeling heteroskedastic data
System of linear regressions
Survey data: Weighting, clustering, and stratification
Stata resources
Exercises
Linear instrumental-variables regressionIV estimation
IV example
Weak instruments
Diagnostics for weak instruments
Formal tests for weak instruments
Better inference with weak instruments
3SLS systems estimation
Stata resources
Exercises
Quantile regressionQR
QR for medical expenditures data
QR for generated heteroskedastic data
QR for count data
Stata resources
Exercises
Linear panel-data models: BasicsPanel-data methods overview
Individual-effects model
Fixed-effects model
Random-effects model
Pooled model or population-averaged model
Two-way–effects model
Mixed linear models
Panel-data summary
Pooled or population-averaged estimators
Within estimator
Between estimator
Between estimator
Application of the xtreg, be command
RE estimator
Comparison of estimators
The hausman command
Robust Hausman test
First-difference estimator
Long panels
Panel-data management
Stata resources
Exercises
Linear panel-data models: ExtensionsPanel IV estimation
Hausman–Taylor estimator
Arellano–Bond estimator
Mixed linear models
Clustered data
Stata resources
Exercises
Nonlinear regression methodsNonlinear example: Doctor visits
Nonlinear regression methods
Different estimates of the VCE
Prediction
Marginal effects
Comparison of calculus and finite-difference methods
Model diagnostics
Stata resources
Exercises
Nonlinear optimization methodsNewton–Raphson method
Core Mata code for Poisson NR iterations
Complete Stata and Mata code for Poisson NR iterations
Gradient methods
The ml command: lf method
Checking the program
The ml command: d0, d1, and d2 methods
The Mata optimize() function
Evaluator program for Poisson MLE
The optimize() function for Poisson MLE
Generalized method of moments
Stata resources
Exercises
Testing methodsCritical values and p-values
Wald tests and confidence intervals
Test single coefficient
Test several hypotheses
Test of overall significance
Test calculated from retrieved coefficients and VCE
Likelihood-ratio tests
Lagrange multiplier test (or score test)
Test size and power
Specification tests
Stata resources
Exercises
Bootstrap methodsBootstrap methods
Bootstrap pairs using the vce(bootstrap) option
Bootstrap pairs using the bootstrap command
Bootstraps with asymptotic refinement
Bootstrap pairs using bsample and simulate
Alternative resampling schemes
The jackknife
Stata resources
Exercises
Binary outcome modelsSome parametric models
Estimation
Example
Hypothesis and specification tests
Lagrange multiplier test of generalized logit
Heteroskedastic probit regression
Goodness of fit and prediction
Marginal effects
Endogenous regressors
The ivprobit command
Maximum likelihood estimates
Two-step sequential estimates
Grouped data
Stata resources
Exercises
Multinomial modelsMultinomial models overview
Multinomial example: Choice of fishing mode
Multinomial logit model
Conditional logit model
Nested logit model
Multinomial probit model
Random-parameters logit
Ordered outcome models
Multivariate outcomes
Stata resources
Exercises
Tobit and selection modelsTobit model
Tobit model example
Left-truncated, left-censored, and right-truncated examples
Left-censored case computed directly
Marginal impact on probabilities
Tobit for lognormal data
Generalized residuals and scores
Test of normality
Test of homoskedasticity
Two-part model in logs
Selection model
Prediction from models with outcome in logs
Stata resources
Exercises
Count-data modelsFeatures of count data
Empirical example 1
Poisson model results
Robust estimate of VCE for Poisson MLE
Test of overdispersion
Coefficient interpretation and marginal effects
NB2 model results
Fitted probabilities for Poisson and NB2 models
The countfit command
The prvalue command
Discussion
Generalized NB model
Variants of the hurdle model
Application of the hurdle model
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
Empirical example 2
The prcounts command
The countfit command
Model comparison using countfit
Models with endogenous regressors
Model and assumptions
Two-step estimation
Application
Stata resources
Exercises
Nonlinear panel modelsNonlinear panel-data overview
FE models
RE models
Pooled models or population-averaged models
Comparison of models
Binary outcome models
Tobit model
Count-data models
Stata resources
ExercisesApendixes
Programming in Stata
Stata matrix commands
Matrix input by hand
Matrix input from Stata estimation results
Programs
Program debugging
Mata
How to run Mata
Mata matrix commands
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
Element-by-element operators
Scalar and matrix functions
Matrix inversion
Creating Stata matrices from Mata matrices
Creating Stata data from a Mata vector
Programming in Mata
Glossary of abbreviations