Articles liés à Applied Microeconometrics Using Stata
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Synopsis
Covering important topics omitted from basic introductions to Stata, Microeconometrics Using Stata shows how to do microeconometric research using Stata. It provides the most complete and up-to-date survey of microeconometric methods available in Stata.
After a brief introduction to Stata, the authors present linear regression, simulation, and generalized least squares methods. The section on cross-sectional techniques is complete with up-to-date treatments of instrumental-variables methods for linear models as well as quantile regression methods. The next section covers estimators for the parameters of linear panel-data models. The book explores standard random-effects and fixed-effects methods, along with mixed linear models used in many areas outside of econometrics. After introducing methods for nonlinear regression models, the authors discuss how to code new, nonlinear estimators in Stata. They show how to easily implement new nonlinear estimators. The authors also cover inference using analytical and bootstrap approximations to the distribution of test statistics. The book then contains a section on methods for different nonlinear models, including multinomial, selection, count-data, and nonlinear panel-data models.
By combining intuitive introductions and detailed discussions of Stata examples, this book provides an invaluable hands-on introduction to microeconometrics.
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Présentation de l'éditeur
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.
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