Mastering 'Metrics: The Path from Cause to Effect

Product Information

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. 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.

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. 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 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. 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 In 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.

Author

Wooldridge, JM (2012), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning. Endnotes 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. As already introduced in the first chapter, treatment and control groups are not necessarily equal in all other aspects, especially under non-randomized conditions. Therefore, the idea of "Regression" is discussed in the next chapter. Regression is presented as a conditioning technique that only delivers credible results if all variables that introduce group differences apart from the treatment are observed. Such variables are then computationally made equal across the groups, so that causal inference can be made. The authors emphasize that, in most natural settings, selection bias can have multiple sources that are usually not all observable. In such cases, the power of regression is limited. Our focus on five core econometric tools is a natural consequence of contemporary econometric practice, which owes little to the formalities of the classical linear regression model, the arcane statistical assumptions of generalised least squares, or the elaborate simultaneous equations framework that fill so many texts. We begin with randomised trials, which set our standard for research validity, moving on to a detailed but model-free discussion of regression, the tool most likely to be used by practitioners. Our regression application — estimating the effects of private college attendance on later earnings — shows the power of regression to turn night into day when it comes to causal conclusions. 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

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 Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect.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. 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 Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. 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

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. With humor and rigor, this book explores key approaches in applied econometrics. The authors present accessible, interesting examples—using data-heavy figures and graphic-style comics—to teach practitioners the intuition and statistical understanding they need to become masters of 'metrics. A must-read for anyone using data to investigate questions of causality!"—Melissa S. Kearney, University of Maryland and the Brookings Institution 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. 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 Angrist, JD, and J-S Pischke (2015), Mastering Metrics: The Path from Cause to Effect, Princeton University Press.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. Angrist, Joshua D. & Pischke, Jörn-Steffen (2015). Mastering 'Metrics: The path from cause to effect. Princeton, Oxford: Princeton University Press, 304 p., 35 USD, ISBN 978-0-691-15284-4 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. Around five years ago, Joshua D. Angrist and Jörn-Steffen Pischke published their first joint book on econometrics tools for causal inference: Mostly harmless econometrics (2009). Although this book is excellent in many regards (e.g., more than 5000 quotes on Google Scholar), it was not as harmless as the title might suggest. Mastering 'Metrics: The path from cause to effect now fills this gap, as it is a truly nontechnical introduction. 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