Mastering 'Metrics: The Path from Cause to Effect

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Written by true 'masters of 'metrics,' this book is perfect for those who wish to study this important subject. Using real-world examples and only elementary statistics, Angrist and Pischke convey the central methods of causal inference with clarity and wit."—Hal Varian, chief economist at Google The writing is lively and engaging, with quotes, anecdotes and jokes scattered throughout. . . . I have become a big fan of this new textbook. . . . In my view, the emphasis on thinking about parameters of interest and identification before discussing technical matters is a huge improvement on traditional teaching approaches. Instructors may have to spend more time preparing lectures and tutorials, but I predict significant benefits in terms of students' learning and appreciation of applied econometrics."—Tue Gørgens, Economic Record Posing several well-chosen empirical questions in social science, "Mastering 'Metrics" develops methods to provide the answers and applies them to interesting datasets. This book will motivate beginning students to understand econometrics, with an appreciation of its strengths and limits."--Gary Chamberlain, Harvard University

There is also an effort at comparison of various techniques and lingering of the IV-2SLS; but I feel either the comparison should have flowed through the entire book, or should have been chapterized separately. In places where the story of a DD is flowing, an IV comparison takes one off guard in terms of now being able to apply and compare.

Endnotes

Modern econometrics is more than just a set of statistical tools—causal inference in the social sciences requires a careful, inquisitive mindset. Mastering 'Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference."—David Deming, Harvard University Hamermesh, DS (2013), “Six Decades of Top Economics Publishing: Who and How?”, Journal of Economic Literature, 162-172. In terms of the chapters itself, I think they are very topical and will cover a lot of the modern research; the book pulls away from a fundamental issue - no matter what the methods are, the thought of comparison and counterfactuals is not emphasized enough I feel. Consider a standard econometrics textbook - say Wooldridge - it actually draws a framework where you know - no matter what the empirical problem is, you need to think in terms of identification, endogeneity and the underlying logic of counter-factuals. They certainly bring in a lot of that - where they talk about apples-to-apples comparison; but the emphasis is not approached as a general method of empirical analysis and the book can go far if that is emphasized. Thus in terms of binding the various methods - (a) a comparison and (b) a generalized empirical strategy might help get the econometrics logic through to a wider audience. This valuable book connects the dots between mathematical formulas, statistical methods, and real-world policy analysis. Reading it is like overhearing a conversation between two grumpy old men who happen to be economists--and I mean this in the best way possible."--Andrew Gelman, Columbia University From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric research

The unapologetic focus on causal relationships that’s emblematic of modern applied econometrics emerged gradually in the 1980s and has since accelerated. 1 Today’s econometric applications make heavy use of quasi-experimental research designs and randomised trials of the sort once seen only in medical research. In fact, the notion of a randomised experiment has become a fundamental unifying concept for most applied econometric research. Even where random assignment is impractical, the notion of the experiment we’d like to run guides our choice of empirical questions and disciplines our use of non-experimental tools and data.

‘Metrics is the original data science

Personally I found the extended metaphor that econometrics is kung fu to be annoying. I think the authors believed that they were making the material more accessible by treating it less reverently, which I agree could have been an effective communication strategy, but I think it mostly fell flat. If I'm cringing at your puns I'm not learning about local average treatment effects. Moreover, I think the metaphor that econometrics is kung fu is actually harmful. Kung fu is mysterious and mystical. It's studied at the feet of a master over the course of a lifetime. The master might have you wash floors for a year, without offering a reason. There is definitely an art to econometrics, but clouding econometrics in mysticism does more to protect the reputation of the teacher than it does to advance the student's learning. Others may disagree but this grasshopper would have preferred we spend less time in the dojo and more time in the computer lab.

We don't want a book which gives us examples and then loses us in these examples. For examples should lead us to building of the concepts and continue our quest forward. " Differences-in-Differences 178 5.1 A Mississippi Experiment 178 5.2 Drink, Drank, ... 191 Masters of 'Metrics: John Snow 204 Appendix: Standard Errors for Regression DD 205 Instrumental Variables 98 3.1 The Charter Conundrum 99 3.2 Abuse Busters 115 3.3 The Population Bomb 123 Masters of 'Metrics: The Remarkable Wrights 139 Appendix: IV Theory 142

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Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect. You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

See, for example, Table 4 in Hamermesh (2013), which highlights the increasing analysis of user-generated data, much coming from experiments and quasi-experimental research designs. The Regression Discontinuity Designs are depicted in chapter 4 and distinguished from the instrumental variables approach. The fact that variables in here have a fixed cutoff point - resulting from an external rule - which either completely determines how a treatment manifests or increases its likelihood, is illustrated. Individuals close to this cut-off can be seen as equal in other characteristics. For example, Angrist and Pischke investigate whether young adults die more often on their 21st birthday. The regression discontinuity in the mortality rate around the birthday is then interpreted as an indicator for the effect of the minimum legal drinking age, defined by law ("Some young people appear to pay the ultimate price for the privilege of downing a legal drink", p. 164). The basic idea why this method is also a robust path to causal inference is explicitly discussed.Or have you wondered why we have to measure weird things (data on quarter of births) to understand the impact of education. These and many other issues which are explored in this book actually bring out a glamorous aspect of the toils economists go through in examining an issue with the precision, care and concern - especially because policies are a result of these studies! It is thus an intersting starting place for beginners too! However, my expectations from this book were more - especially since I like the papers written by Angrist etc. Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175 First, the content. Mastering 'Metrics does a pretty good job of covering the intuition (and some of the math) behind random assignment, regression, instrumental variables, regression discontinuity designs, and difference in differences. I think their treatment of these topics would be most useful to someone who was trying to read modern applied econometrics (or political science). Ideally the reader would have taken enough statistics that they can focus on trying to grasp the concept of potential outcomes rather than trying to work through the algebra. The methods that are covered are extremely important in social science and so having an idea of what they do and why we use them is helpful.