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Mastering 'Metrics: The Path from Cause to Effect

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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. Gary King, Harvard University "This valuable book connects the dots between mathematical formulas, statistical methods, and real-world policy analysis.

Or have you wondered why we have to measure weird things (data on quarter of births) to understand the impact of education. Metrics, that is, the field of applied econometrics, encompasses the statistical methods economists use to untangle cause and effect in human affairs. One of the many strengths of Mastering ‘Metrics is the use of various cases to illustrate problems, tools and outcomes … the lucid and intuitive approach used by the authors is refreshing compared with the text-book style of writing we have become accustomed to in ‘metrics. If someone makes a decision between two competing choices, selection bias can intervene - for example, an average person going to a hospital is actually unhealthier than someone who doesn’t go, but that is because we are looking at two different types of people! Wonderful companion to anyone working through an introductory Python or R course, as the technical implementations would complement this book's explanations well.

Another example relates to health insurance - at first glance, those with insurance seem healthier than those that don’t have it. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Twins are used to examine schooling outcomes as they help keep ability fixed - identical twins are said to be similarly abled. There's nothing wrong with aiming econometrics at data scientists, in fact I think there is a lot that they can and should learn from each other. 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!

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. Statistics is second only to accounting among the technical skills necessary for engaging in modern finance and is fundamental for anyone who considers quantitative work a core element of finance.Mastering ‘Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference. Take for instance - Chapters on Regression, RDD are flowing smoothly, but the chapter on IV is tighter than the others. A new book by Joshua Angrist and Jörn Steffen Pischke shows how the five core econometric tools -randomised trials, regression, instrumental variables, regression discontinuity designs and differences-in-differences - accomplish this. The ascendance of the five core econometric tools – experiments, matching and regression methods, instrumental variables, differences-in-differences and regression discontinuity designs – marks a paradigm shift in empirical economics. The authors discuss regression unconventionally by showing how to get to the point where the key economic phrase ceteris paribus (other things being equal) can be applied.

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