I feel like I'm having a conversation with a very knowledgeable fellow rather than being lectured and constantly reminded that I have a long way to go before I am an expert. This is a universal property – one that is reliably true – and we don’t intimidate readers with descriptions of the punishments to be meted out for the failure of classical assumptions. Tue G rgens, Economic Record "I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights. We’re happy to join these colleagues in an effort to polish and renew our profession’s rusty instructional canon.

Through careful descriptions of underlying principles, Angrist and Pischke clearly take some of the con out of econometrics. 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. I suspect that the assumption is that the reader will next go pick up their other book "Mostly Harmless Econometrics" but that is never explicitly stated. Wooldridge, JM (2012), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning.

ust ust over a quarter century ago, Edward Leamer (1983) reflected on the state of empirical work in economics.

I believe that this book was written by economists who see data science expanding into areas that they consider to be economics and are uncomfortable with this development. 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. Mastering 'Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference. Just like healthcare, Steve suggests there is little indication that the more you spend on education the greater the eventual benefit - however that benefit may be measured.By bidding on, or purchasing this item, you are agreeing to us sharing your name and address details with that 3rd party supplier to allow us to fulfil our contractual obligations to you. Take for instance - Chapters on Regression, RDD are flowing smoothly, but the chapter on IV is tighter than the others.

By using the Web site, you confirm that you have read, understood, and agreed to be bound by the Terms and Conditions. However, my expectations from this book were more - especially since I like the papers written by Angrist etc. I'd say you should look elsewhere if the finer points of regression and OLS are not fresh on your mind.Measuring these before-and-after effects is the foundation of any event study but, again, requires careful structure and control for other factors. With an engaging, insightful style, Angrist and Pischke catch readers up on five powerful methods in this area.

Recommended for the process of breaking policy studies into digestible pieces and understanding how it all fits together, and how to communicate and discuss with others. Gary King, Harvard University "This valuable book connects the dots between mathematical formulas, statistical methods, and real-world policy analysis. 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. Of course, students who attend relatively selective private schools are likely to have higher earnings for many reasons – this is the selection bias that plagues most naive comparisons. Economists view data scientists as regression monkeys (probably the worst insult you can give someone in economics).R., Financial Analysts Journal "Focusing on five econometric tools, Mastering 'Metrics presents key econometric concepts. I found it incredibly accessible and readable, which sets it well apart from pretty much every econometrics textbook on the market. The material covered on causal inference is as follows: randomized trials, regression, instrumental variables, regression discontinuity design, and differences-in-differences. Although instrumental variable regression was invented as a solution to the problem of estimating supply and demand curves, our take on instrumental variables corresponds to the way instrumental variable regression is most often used now: as a solution to the problem of selection bias.