Microeconometrics Using Stata, Second Edition, Volumes I and II
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Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to p...
Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.
Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.
The second edition is greatly expandedthe new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on
Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.
Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.
The second edition is greatly expandedthe new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on
- duration models
- randomized control trials and exogenous treatment effects
- endogenous treatment effects
- models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models
- spatial autoregressive models
- semiparametric regression
- lasso for prediction and inference
- Bayesian analysis
Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.
- A required reference for any applied researcher
- Learn the statistics and econometric theory underlying the methods used in modern economic research
- See examples of how to implement both classic and modern econometric techniques in Stata---from directly using Stata commands such as those for fitting linear and nonlinear models to writing your own simulations or optimization routines for new methods
- New in the second edition: duration models exogenous and endogenous treatment-effects estimation models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference and Bayesian analysis
- A fantastic tool for teaching it covers both theory and applications relevant to any modern researcher.