1. Microeconometrics using Stata [2010]
 Cameron, A. Colin (Adrian Colin)
 Rev. ed. 2010  College Station, Tex. : Stata Press, c2010.
 Description
 Book — xlii, 706 p. : ill ; 24 cm.
 Summary

 Stata Basics Interactive use Documentation Command syntax and operators Dofiles and log files Scalars and matrices Using results from Stata commands Global and local macros Looping commands Some useful commands Template dofile Userwritten commands
 Data Management and Graphics Introduction Types of data Inputting data Data management Manipulating datasets Graphical display of data
 Linear Regression Basics Introduction Data and data summary Regression in levels and logs Basic regression analysis Specification analysis Prediction Sampling weights OLS using Mata
 Simulation Introduction Pseudorandomnumber generators: Introduction Distribution of the sample mean Pseudorandomnumber generators: Further details Computing integrals Simulation for regression: Introduction
 GLS Regression Introduction GLS and FGLS regression Modeling heteroskedastic data System of linear regressions Survey data: Weighting, clustering, and stratification
 Linear InstrumentalVariables Regression Introduction IV estimation IV example Weak instruments Better inference with weak instruments 3SLS systems estimation
 Quantile Regression Introduction QR QR for medical expenditures data QR for generated heteroskedastic data QR for count data
 Linear PanelData Models: Basics Introduction Paneldata methods overview Paneldata summary Pooled or populationaveraged estimators Within estimator Between estimator RE estimator Comparison of estimators Firstdifference estimator Long panels Paneldata management
 Linear PanelData Models: Extensions Introduction Panel IV estimation HausmanTaylor estimator ArellanoBond estimator Mixed linear models Clustered data
 Nonlinear Regression Methods Introduction Nonlinear example: Doctor visits Nonlinear regression methods Different estimates of the VCE Prediction Marginal effects Model diagnostics
 Nonlinear Optimization Methods Introduction NewtonRaphson method Gradient methods The ml command: lf method Checking the program The ml command: d0, d1, d2, lf0, lf1, and lf2 methods The Mata optimize() function Generalized method of moments
 Testing Methods Introduction Critical values and pvalues Wald tests and confidence intervals Likelihoodratio tests Lagrange multiplier test (or score test) Test size and power Specification tests
 Bootstrap Methods Introduction Bootstrap 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
 Binary Outcome Models Introduction Some parametric models Estimation Example Hypothesis and specification tests Goodness of fit and prediction Marginal effects Endogenous regressors Grouped data
 Multinomial Models Introduction Multinomial models overview Multinomial example: Choice of fishing mode Multinomial logit model Conditional logit model Nested logit model Multinomial probit model Randomparameters logit Ordered outcome models Multivariate outcomes
 Tobit and Selection Models Introduction Tobit model Tobit model example Tobit for lognormal data Twopart model in logs Selection model Prediction from models with outcome in logs
 CountData Models Introduction Features of count data Empirical example 1 Empirical example 2 Models with endogenous regressors
 Nonlinear Panel Models Introduction Nonlinear paneldata overview Nonlinear paneldata example Binary outcome models Tobit model Countdata models
 Appendix A: Programming in Stata Appendix B: Mata
 Glossary References Author Index Subject Index Stata resources and Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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