Metrics, that is, the field of applied econometrics, encompasses the statistical methods economists use to untangle cause and effect in human affairs. The presentation and exposition on the remaining three topics are very good but the authors seem to gloss over some detail for the sake of brevity which will make the text less than ideal for true beginners in econometrics.

As data sets have grown bigger and more complex, the demand for new grads with data-analytic skills has accelerated rapidly. Written by true 'masters of 'metrics,' this book is perfect for those who wish to study this important subject. I started reading this book as a 'forced formula' in a standard Labor Economics course but never ended up reading the whole thing, until I decided to pick it up again.We don’t have an experiment, of course, so we must use the data we have to undertake the best “apples-to-apples” comparison we can. 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 promote the highest ethical standards and offer a range of educational opportunities online and around the world. By contrast, Hill, Griffiths, and Lim (2011) introduce instrumental variable regression in an intimidating-sounding chapter on ‘Random Regressors and Moment-Based Estimation’. Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect. 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.Building on my introductory data analysis course, some of this is review, but in the context of bridging the quantitative we learned last quarter and connecting it accurately and thoroughly to the qualitative policy analysis we've been working on this quarter, its utility is essential. 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. Becker, WE, and WH Greene (2001), “Teaching Statistics and Econometrics to Undergraduates”, Journal of Economic Perspectives, 169-182. When I build up the courage to tackle an academic and educational book, these are the types of books I look for. We explain why these questions are challenging and why simple empirical strategies to address them can be misleading.

Although instrumental variables can be used as a technique to generate better estimates, there are subtleties and technical difficulties in the use of this technique. 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.Mastering 'Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference.