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, by Jörn-Steffen Pischke Joshua David Angrist
Ebook Free , by Jörn-Steffen Pischke Joshua David Angrist
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Product details
File Size: 21690 KB
Print Length: 295 pages
Publisher: Princeton University Press; with French flaps edition (December 21, 2014)
Publication Date: December 21, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00MZG71MC
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#81,206 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Imagine that you are taking a senior undergraduate seminar class, and the professor has picked out about a dozen recent social science papers (1/3 authored by Angrist) with econometric analyses in order to teach the various statistical methods that you learned in your econometrics class. That is what this book feels like. The topics of the papers try to answer causal questions such as, “Does low cost or free health insurance improve health outcomes?â€, “Does attending a highly selective private university lead to higher earnings compared to a less competitive public university?â€, and “Do charter schools increase student test scores?†The authors introduce the background for each paper then describe their favored econometric techniques to determine the answers (randomized trial, regression, instrumental variable, regression discontinuity, and differences-in-differences). The first 3 techniques are covered extensively in most introductory econometrics textbooks, while the last 2 are much less covered. A few other econometrics topics are sprinkled throughout the text.For those with an economics degree or a very good grasp of statistical analysis, this book can be a nice refresher on econometric techniques used to determine causal effects through experiments or quasi-experiments. A more advanced treatment (with linear algebra and calculus) of the same topics can be found in the authors’ other book “Mostly Harmless Econometricsâ€.For those that are upper level economics or social science majors in college, this book can serve as a supplement to an econometrics or advanced statistics class by providing real examples of econometrics in action and act as a bridge to understanding econometrics research articles. The book seems to be aimed at the college student that has had at least 1-2 classes of college level statistics. Even though the hard core math that would be found in an econometrics textbook is left to the appendices, there are plenty of equations and mathematical constructs in the main text that require a fairly solid understanding of math to fully appreciate the analysis.For the general public that may have had a statistics class in high school or less, the analysis will likely be too difficult, however the introductory comments and conclusions for each paper may be of interest. This book is not likely to be found in most public libraries. There is some humor and historical notes to offset the heaviness of the material.
This is watered down version of the book the authors wrote about econometrics (Mostly Harmless Econometrics). If you are just starting to explore the world of econometrics, and the tools and techniques that are used, this is a great book to start with. I also have the other book and do have to agree that the original book was a much harder read than the one I am reviewing.
This book is the perfect companion guide for economists (or any social scientists, really) who are early in the process of learning econometrics or statistics. Well-written, it's an entertaining study into what can be a very dry topic so props to Angrist and Prishke for writing a good book. This book will not replace an econometrics course, but if you're like me who took the course years ago and needed a refresher, this is perfect. Or, it can be used to supplement and better understand your current course. Further, by looking at actual studies, Angrist and Prishke make the move from abstract into useful for regressions and the like.I highly recommend. I use this (and their companion Mostly Harmless Econometrics) in my PhD econometrics course
I couldn't express how much appreciation I have on this book -- it is written by true masters in a most accessible way for readers to get the essence and spirit of applied empirical work to investigate causal relationship.Econometrics is hard to learn. It is rare to find textbooks teaching econometrics in an intuitive, accessible and empirical and application focused way. This book really fills the gap.The authors also wrote Mostly Harmless Econometrics (MHE), which is a more advanced treatment mostly for graduate students and researchers. I would highly recommend reading this book first.Lastly, thank you the two masters writing this book. It is remarkable. I wish they keep writing another book that is filling the gap between this one and MHE.
This is a really good introduction to applied econometrics. The authors elect for a more intuitive approach than they take in their earlier book, Mostly Harmless Econometrics. The result is an entertaining and easy read that provides a nice conceptual understanding of the ideas behind applied econometrics. This would be a good book to read over the summer before taking an course in econometrics if you are completely new to the field. Readers with more experience and/or knowledge in the field will find the book a little light on the technical details, but nonetheless very interesting and useful as a refresher on the conceptual stuff.
Very good introductory book to econometrics. I am a graduate student in a PhD Econ program and it helped me even to understand some of the concepts covered in my courses. I think this would be a good supplement for any undergraduate or graduate level econometrics course
The Kindle version is easy to read (equations are clear and text resizing works well). Excellent for a quick course for uninterested undergrads/masters of something other than economics. Their use of the word "residual" seems nonstandard and could cause confusion.
A very well-written book. Clear, interesting and an enjoyable read. There is sufficient mathematical formulaic representations without overwhelming those who are new to the literature. For those who have a good background, it enhances the comprehensibility of the connections between formulas and methods involved in causal inferential statistics.
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