Microeconometrics Using Stata

Microeconometrics Using Stata PDF

Author: Adrian Colin Cameron

Publisher:

Published: 2009

Total Pages: 738

ISBN-13:

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This outstanding introduction to microeconometrics research using Stata offers the most complete and up-to-date survey of methods available. The authors address each topic with an in-depth example and demonstrate how to use Stata's programming features to implement methods for which the application does not have a specific command.

Microeconometrics

Microeconometrics PDF

Author: A. Colin Cameron

Publisher: Cambridge University Press

Published: 2005-05-09

Total Pages: 1058

ISBN-13: 1139444867

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This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

Applied Microeconometrics Using Stata

Applied Microeconometrics Using Stata PDF

Author: Adrian Colin Cameron

Publisher: Stata Press

Published: 2008-12-15

Total Pages:

ISBN-13: 9781597180160

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Covering important topics omitted from basic introductions to Stata, Microeconometrics Using Statashows 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 bookprovides an invaluable hands-on introduction to microeconometrics.

Multilevel and Longitudinal Modeling Using Stata, Volumes I and II

Multilevel and Longitudinal Modeling Using Stata, Volumes I and II PDF

Author: S. Rabe-Hesketh

Publisher:

Published: 2021-10-22

Total Pages: 1098

ISBN-13: 9781597181365

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"Multilevel and Longitudinal Modeling Using Stata, Fourth Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels. Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. Across volumes, the 16 chapters, over 140 exercises, and over 110 datasets span a wide range of disciplines, making the book suitable for courses in the medical, social, and behavioral sciences and in applied statistics. This first volume is dedicated to models for continuous responses and is a prerequisite for the second volume on models for other response types. It has been thoroughly revised and updated for Stata 16. New material includes the Kenward-Roger degree-of-freedom correction for improved inference with a small number of clusters, difference-in-differences estimation for natural experiments, and instrumental-variable estimation to handle level-1 endogeneity"--

Meta-analysis

Meta-analysis PDF

Author: Ralf Schulze

Publisher: Hogrefe Publishing

Published: 2004

Total Pages: 264

ISBN-13:

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Meta-analysis has become the standard method for summarizing research findings in many scientific fields. This book provides a comprehensive treatment and comparison of the statistical procedures available for meta-analysis with correlations as effect sizes.

Mostly Harmless Econometrics

Mostly Harmless Econometrics PDF

Author: Joshua D. Angrist

Publisher: Princeton University Press

Published: 2009-01-04

Total Pages: 392

ISBN-13: 0691120358

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In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous.

Computer Age Statistical Inference

Computer Age Statistical Inference PDF

Author: Bradley Efron

Publisher: Cambridge University Press

Published: 2016-07-21

Total Pages: 496

ISBN-13: 1108107958

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The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.