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Mostly Harmless Econometrics: An Empiricist's Companion
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Pressestimmen
A quirky and thought-provoking read for any budding econometrician... Insightful and refreshing. -- James Davidson, Times Higher Education I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation ... an excellent guide to how to do basic regression/IV/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses. -- Diane Coyle, The Enlightened Economist Blog The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences. -- Pavel Stoynov, Zentralblatt MATH [T]he matter covered in the book is surely of interest to most agricultural economists. Even if it is not a complete overview of existing econometric research methods, it certainly contains a good deal of hands on advice driven by years of experience. -- "European Review of Agricultural Economics This book is an extremely thought-provoking contribution to the literature. It champions a different paradigm to that characterising most econometrics texts and does so with considerable (idiosyncratic) style and grace. Highly recommended! -- David Harris and Christopher L. Skeels, Economic Record
Kurzbeschreibung
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 Jrn-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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Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis
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14 von 14 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
True, this book is not meant to be an introduction to econometrics and I agree that you should already have a sound knowledge of undergraduate econometrics (e.g. at the level of Wooldridge). If you already know what OLS and IV are supposed to mean, then you are ready to read this book.
Anyway, the book does never claim to be an easy starter, but rather an "empiricist's companion". Thus, the book should be measured against the claim of being a book helpful for people who are doing research with actual data. Anyone who has ever read a book on econometrics knows that most of these books are actually written by researchers who are doing econometrics WITHOUT any data at all, or as if econometrics was not about data analysis in the end. I guess that's why most students have a hard time bringing the methods to the data (even if they grasp the theory). This book is different from most (actually all) other econometric books I know because it assumes that you mainly want to apply econometric methods to answer some interesting and substantive question in a convincing way.
Even though this focus is very different from the focus of most other econometric books, it actually mirrors what applied researchers actually do. This is true for labor economists most notably, but also for an increasing number of social scientists from other fields such as development economics, health economics, sociology and political science. For example, you will learn about regression discontinuity designs and quantile regression models. These methods are used increasingly in applied research. Nonetheless, "conventional" econometrics texts usually don't say much if anything about these methods.
Overall, anyone who wants to do serious empirical work and has the required background will find this book not only a very valuable resource, but also an entertaining one. This, I think, is more than you can hope for from a book about econometrics.
Anyway, the book does never claim to be an easy starter, but rather an "empiricist's companion". Thus, the book should be measured against the claim of being a book helpful for people who are doing research with actual data. Anyone who has ever read a book on econometrics knows that most of these books are actually written by researchers who are doing econometrics WITHOUT any data at all, or as if econometrics was not about data analysis in the end. I guess that's why most students have a hard time bringing the methods to the data (even if they grasp the theory). This book is different from most (actually all) other econometric books I know because it assumes that you mainly want to apply econometric methods to answer some interesting and substantive question in a convincing way.
Even though this focus is very different from the focus of most other econometric books, it actually mirrors what applied researchers actually do. This is true for labor economists most notably, but also for an increasing number of social scientists from other fields such as development economics, health economics, sociology and political science. For example, you will learn about regression discontinuity designs and quantile regression models. These methods are used increasingly in applied research. Nonetheless, "conventional" econometrics texts usually don't say much if anything about these methods.
Overall, anyone who wants to do serious empirical work and has the required background will find this book not only a very valuable resource, but also an entertaining one. This, I think, is more than you can hope for from a book about econometrics.
2 von 19 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
Don't even think of reading this book - unless you have a solid mathematical background!
This book presents heavy stuff - it is not meant to be an introduction to the topic!
This book presents heavy stuff - it is not meant to be an introduction to the topic!
Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
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22 Rezensionen
41 von 49 Kunden fanden die folgende Rezension hilfreich
Finally a useful econometrics book...
30. Januar 2009
Format:Taschenbuch
This is a wonderful book. Despite having taken many courses and read many statistics and econometrics books, I'm sometimes stuck in my own applied problems. Of course, these courses and books taught me how to derive asymptotic theorems or be careful when maximum likelihood fails, but this is not what I really need to know to solve my problems. Now with Angrist and Pischke, I have a book that is truly applied in focus -- one that explains why and how certain empirical strategies are convincing and one that is up-to-date with the latest examples of recent research employing these strategies. Real econometrics applied to real problems. This book should be on every applied economists bookshelf.
35 von 42 Kunden fanden die folgende Rezension hilfreich
Mixes issues
9. März 2010
Format:Taschenbuch
In their introduction Angrist and Pischke say "empirical research is most valuable when it uses data to answer specific causal questions, as if in a randomized clinical trial". The book aims to teach this approach to students of applied econometrics.
The emphasis on natural experiments and quasi-experiments which the authors espouse has become influential in some sub-areas of econometrics and the authors, particularly Angrist, have played a leading role in this development. However, this approach is not uncontroversial. The Journal of Economic Perspectives has an entire issue (Spring 2010, Vol. 24, No. 2, full text online for public) discussing the pros and cons and you may want to glance through it before buying this book.
Taken on its own terms, the book intermingles four levels of discussion: the philosophical and methodological issues around causality, tips and tricks on how the apply the workhorse models of (micro)econometrics, case studies, and the mathematical properties of models and estimators. This intermingling may be useful for a practitioner trying to see the big picture, but it makes things hard for a beginning student. The problem is compounded by the sketchiness of the mathematical derivations.
If you are starting out in econometrics you may be better served by traditional textbooks with more detailed and linear presentations such as Wooldridge's Introductory Econometrics and Econometric Analysis of Cross Section and Panel Data.
If your interest is primarily in causal inference, book-length treatments that focus only on that aspect can be found in Judea Pearl's Causality: Models, Reasoning and Inference or Morgan and Winship's Counterfactuals and Causal Inference.
The emphasis on natural experiments and quasi-experiments which the authors espouse has become influential in some sub-areas of econometrics and the authors, particularly Angrist, have played a leading role in this development. However, this approach is not uncontroversial. The Journal of Economic Perspectives has an entire issue (Spring 2010, Vol. 24, No. 2, full text online for public) discussing the pros and cons and you may want to glance through it before buying this book.
Taken on its own terms, the book intermingles four levels of discussion: the philosophical and methodological issues around causality, tips and tricks on how the apply the workhorse models of (micro)econometrics, case studies, and the mathematical properties of models and estimators. This intermingling may be useful for a practitioner trying to see the big picture, but it makes things hard for a beginning student. The problem is compounded by the sketchiness of the mathematical derivations.
If you are starting out in econometrics you may be better served by traditional textbooks with more detailed and linear presentations such as Wooldridge's Introductory Econometrics and Econometric Analysis of Cross Section and Panel Data.
If your interest is primarily in causal inference, book-length treatments that focus only on that aspect can be found in Judea Pearl's Causality: Models, Reasoning and Inference or Morgan and Winship's Counterfactuals and Causal Inference.
14 von 15 Kunden fanden die folgende Rezension hilfreich
Essential reading, though imperfect
14. Oktober 2010
Format:Taschenbuch|Von Amazon bestätigter Kauf
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.
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.
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