Mastering 'Metrics: The Path from Cause to Effect

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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. 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. So i have almost reached halfway chapter 4 where RDD is being discussed. I found the chapters imbalanced. Like the IV chapter was very heavy and was not a smoother flow like the other ones. 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

Angrist, JD, and J-S Pischke (2015), Mastering Metrics: The Path from Cause to Effect, Princeton University Press.Few fields of statistical inquiry have seen faster progress over the last several decades than causal inference. With an engaging, insightful style, Angrist and Pischke catch readers up on five powerful methods in this area. If you seek to make causal inferences, or understand those made by others, you will want to read this book as soon as possible."—Gary King, Harvard University 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. Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175

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 The chapters I feel are also imbalanced. Take for instance - Chapters on Regression, RDD are flowing smoothly, but the chapter on IV is tighter than the others. On the merit of how much does the book intend to give the reader the details on these things is another issue. But given a cursory exposition on this, I think IV overdoes it, whereas other chapters are more pointed and do not bring out unnecessary details. Wooldridge, JM (2012), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning. EndnotesIn our experience, most econometrics teachers enjoy working with data, and they hope and expect that their students will too. Yet, a sad consequence of the inherited econometrics canon is its drabness. This is really too bad because modern applied econometrics is interesting, relevant, and, yes, fun! Instructors who have as much fun teaching econometrics as they do when they use it in their research can hope to transmit their excitement to their students. In addition to having a good time, we plant the seeds of useful data analysis in the next generation of scholars, policy-makers, and an economically literate citizenry. The promise of our approach to instruction is evident in the popularity of the Freakonomics franchise and in the sparkling new intro-to-economics principles book by Acemoglu, Laibson, and List (2015): their take on economics puts questions and evidence ahead of abstract models. We’re happy to join these colleagues in an effort to polish and renew our profession’s rusty instructional canon. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. 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 The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. The Global Crisis provoked some to ask, “what’s the use of economics”?, a reference to the economics that most economists had studied in college. We’d pile on, adding, “what’s the use of econometrics… at least as currently taught”? Most of the undergraduates who major in economics take a course in econometrics. This course should be one of the more useful experiences a student can have. For decades, economics undergraduates have found jobs in sectors that make heavy use of quantitative skills. As data sets have grown bigger and more complex, the demand for new grads with data-analytic skills has accelerated rapidly. Econometrics courses promise to equip our students with the powerful tools economists use to understand the economic relationships hidden in data. It’s both remarkable and regrettable, therefore, that econometrics classes continue to transmit an abstract body of knowledge that’s largely irrelevant for economic policy analysis, business problems, and even for much of the econometric research undertaken by scholars.

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 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

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. Economists view data scientists as regression monkeys (probably the worst insult you can give someone in economics). When they look at data science they just see extremely elaborate efforts at curve fitting. Since economists don't think curve fitting is all that interesting or useful for doing economics, they scoff at neural networks and boosting. Imagine their horror when they see data science moving into their territory. The first chapter Randomized Trials outlines basic experimental concepts like treatment, outcome, control and treatment group, the fundamental problem that we can always only observe one reality in one person, and the idea that randomization makes "other things equal" (p. xii). It also points out why perfect randomization is difficult to achieve in real life. Furthermore, the issue of statistical significance in the interpretation of results is discussed, as analyses are usually only based on samples drawn from populations.

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 Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect. I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights."— Financial Analysts Journal

Not just for linear models

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