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Microeconometrics Using Stata: Revised Edition Paperback – March 9, 2010
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The examples are great. For instance, in section 5.3 "Modeling heteroskedastic data" using GLS, the authors describe two different ways to deal with heteroskedasticity: robust standard errors and FGLS. First they walk you through creating a simulated dataset, performing OLS estimation, then performing OLS with heteroskedastic-robust standard errors (using the "robust" option), and compare the results. Next they show you how to plot the errors to visualize the nature of the heteroskedasticity, as well as how to perform different tests for heteroskedasticity in Stata (and which ones assume normality). Then they walk you through step-by-step how to implement FGLS (the second method), concluding with a discussion of how to augment FGLS with weighted least squares for robustness.
I love that it is concise but thorough, and very well organized. The cross-references help a lot when the authors refer to something that they've explained in more detail elsewhere.
I only wish I had discovered it during my first semester of graduate level econometrics. I highly recommend it, and think it would be great if professors required it (or at least encouraged their students to get it). It's appropriate for upper-level undergraduate or master's level econometrics classes.
It is a well written book, and it is very easy to read it. This book is a "how to do" book. You will not find a lot of long explanations of different econometric concepts in it, instead you will find a brief and straight to the point information and guidelines plus tons of examples! All the examples are accompanied with STATA code, so you always know how to get the results that you see in the book, and as a result it is always easy to adjust the code for your needs!
I would definitely recommend this book to all applied econometricians who use STATA as well as to people who want to learn how to use STATA!
Coupled with the help guide already installed in STATA 11, this book is perfect for taking data from a raw form and transferring it into the most up-to-date models. The applications go much further than a grad school textbook.