Mastering 'Metrics: The Path from Cause to Effect

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Admitting that the academic way keeps the writing clean, but then it also makes the reader lose interest. The snippets are like the buzz generators - they are the interest makers - and this book could have gone a long long way in making 'Metrics fun!. 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 Focusing on five econometric tools, Mastering 'Metrics presents key econometric concepts. Any field that uses statistical techniques to conduct causal inference will find this book useful."—Melvyn Weeks, University of Cambridge

Wooldridge, JM (2012), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning. Endnotes Another relevant factor with the book is that the passages do not lead you to read on - rather they are too academic! If the intention is for a wider audience and for a more diversified crowd, then the importance of leading readers onto the next issues is of supreme importance. For eg: they are discussing an issue and then the next issue comes up as a next section. There is no sense of direction as to why am I reading about an issue and where do the connections matter - in terms of comprehending the entire topic, the reader is left on his own. 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 researchData scientists, on the other hand, don't often think about economics at all. From their perspective the two disciplines have basically no overlap. So they struggle to see why they should care about what an economist has to say about anything. This is primarily driven by the popular misperception of economics being about business questions. Imagine their frustration when economists start telling them that their results are wrong. 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.

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 fact that there are not endless instrumental variables given in all areas of interest, often makes it necessary to use other approaches like Differencesin-Differ enees, which is illustrated in chapter 5. The authors explain how developments of control and treat- ment groups can indicate treatment effects, even in the absence of randomization. The approach assumes that even if groups differ in the outcome from the very beginning, a non-parallel development of the groups can be attributed to the treatment, which is again illustrated clearly using econometric examples. 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. 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.

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 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.