Jörn-Steffen Pischke is professor of economics at the London School of Economics and Political Science.

Leamer was not alone; Hendry (1980), Sims (1980), and others writing at about the same time were similarly disparaging of empirical practice. Posing several well-chosen empirical questions in social science, Mastering 'Metrics develops methods to provide the answers and applies them to interesting datasets. In his own words, “fruitful application does not demand extraordinary mathematical sophistication” but simply firm evidence-based foundations. Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect. I do hope that most of my time in my data career I can jump straight to the gold standard of randomized controlled tests, but sometimes that's not feasible, and this book offers a wealth of alternative solutions.The material covered on causal inference is as follows: randomized trials, regression, instrumental variables, regression discontinuity design, and differences-in-differences. Read more about the condition New: A new, unread, unused book in perfect condition with no missing or damaged pages. I also appreciate the asides that provide historical context on the past masters who developed these techniques.

Wielding econometric tools with skill and confidence, Mastering ‘Metrics uses data and statistics to illuminate the path from cause to effect. Here again, the authors focus on thinking through the formulation of tests and when to use these techniques. But where it really stands out is making it personable, drawing you into the book and helping you engage in a learning process that is quite steep. 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.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.

The first empirical application in Hill, Griffiths, and Lim (2011: 49) explores the correlation between food expenditure and income. Johnston describes multivariate regression models as “fitting the regression plane” a technical extension of the two-variable model that fits a line. Many of you are probably thinking that statement doesn't set a very high bar, because most econometrics books are drier than a particular arid corner of the Sahara, and almost impenetrable to anyone without high-level mathematics skills. In the fourth section, the authors cover how breaks and discontinuities offer opportunities for uncovering useful information.

Albeit generally unrelated to finance, these are all fascinating topics for anyone interested in critical policy issues. 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. After a brief discussion of curve fitting, Pindyck and Rubinfeld’s (1976) first edition book began with subsections titled ‘The Model’, ‘Statistical Properties of Estimators’, and ‘Best Linear Unbiased Estimation’. Written by true 'masters of 'metrics,' this book is perfect for those who wish to study this important subject.