Mostly Harmless Econometrics: An Empiricist's Companion

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		Mostly Harmless Econometrics: An Empiricist's Companion
		Mostly Harmless Econometrics: An Empiricist's Companion

The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. * An irreverent review of econometric essentials * A focus on tools that applied researchers use most * Chapters on regression-discontinuity designs, quantile regression, and standard errors * Many empirical examples * A clear and concise resource with wide applications

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The first thing I want to say is this: If you plan on doing regression analysis in your research, stop what you are doing, and read this book first. I think this book represents THE current statement on how we should use regression. For Angrist and Pischke, regression is a technology for summarizing data. If regression is to be used for causal inference, then there is nothing in the specification of the model or the choice of estimator that can ultimately make the causal story persuasive. That is, you don't identify causal effects simply by including "control" variables in your regression. The identification comes from elsewhere---either a real or "quasi" experiment---and the regression is what you use to clean up the imperfections of the experiment and measure effects. Angrist and Pischke have done an enormous service to social science by writing a regression textbook that nonetheless emphasizes the primacy of design. This is a terrific corrective for the "101 flavors of regression" approach of textbooks to date.

Even with this emphasis on design, Angrist and Pischke show us that are a lot of nuances to the way that regressions measure such effects---e.g., in the presence of effect heterogeneity---and that's what this book explores in exquisite detail. It's a hugely important book and a very serious and rigorous treatment, despite it's apparently causal style. They make some claims that may strike some as outrageous---e.g., always using OLS, even for limited dependent variables---but the rigor of their presentation means that the onus is on those who disagree to think harder about why, exactly, they would prefer, say, a more parametric approach.

Nonetheless, it isn't a "5 star" book. It often feels a bit rough-draft-like. The presentation of technical material skips important steps rather haphazardly. I wonder if this was due to bad editing? Hopefully there will be a second edition that cleans up these rough edges, in which case it would be the ideal textbook on regression analysis.

For the life of me, I cannot figure out why this book is recommended as often, or as highly, as it is. If nothing else, it needs to be noted that this book should be used in conjunction with other books to fill in gaps or provide additional case studies for clarification.

Go get an applied econometrics book that pairs with data and software, then go grab Kennedy and Hayashi or something. You'll be much better served.

This book walks you through all kind of pitfalls that may (do) occur in applied econometric work and shows you how with the aid of a few assumptions and a few tests (that you know the result of before you start) you can ignore those issues and proceed anyway. Which, is what you're really after if you're doing applied econometrics anyway right?

I'm the sort of person who's really not supposed to be reading this. They intend it for grad students in econometrics and I'm a college dropout with almost no statistical knowledge. I nonetheless found it interesting and informative. While it didn't take me from nowhere to being a productive researcher, I did find it very useful combined with other sources. The presentation is clear and the examples are all interesting. Recommended.

it is a great book for someone who is looking for real life econometrics. Critics would argue that is not as complete as other books such as Wooldrige but that is actually the point: their target is for practitioner rather than "pure" researcher.

Clear and well-motivated presentation of content. However, binding seems to be poor, as pages started falling out almost immediately and worsening ever since despite very light use over the past two months (book was opened about once per week).

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