Mastering 'Metrics: The Path from Cause to Effect

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Leamer was not alone; Hendry (1980), Sims (1980), and others writing at about the same time were similarly disparaging of empirical practice. It is great that the author separate mathematical derivation and concept intuition, it really helps in understanding the concept.

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.

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. But given a cursory exposition on this, I think IV overdoes it, whereas other chapters are more pointed and do not bring out unnecessary details. If you seek to make causal inferences, or understand those made by others, you will want to read this book as soon as possible. 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. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

Take for instance - Chapters on Regression, RDD are flowing smoothly, but the chapter on IV is tighter than the others.In his own words, “fruitful application does not demand extraordinary mathematical sophistication” but simply firm evidence-based foundations.

this book has been very well received and is hard to fault … what students will learn most is that Econometrics is not only an empirical science, but also an art to be mastered. As data sets have grown bigger and more complex, the demand for new grads with data-analytic skills has accelerated rapidly.Nevertheless, analyzing differences in differences focuses on a perspective that is seldom directly described. These questions are then answered with data and a focussed and carefully executed empirical analysis. It follows and complements a recent Journal of Econometrics special issue also in honor of William A.

Mastering ‘Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference. No one willingly reads a book on econometrics to close this knowledge gap, so technical skills atrophy rather than advance with money management experience. 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. Hal Varian, chief economist at Google "Few fields of statistical inquiry have seen faster progress over the last several decades than causal inference.The winds of change have blown most strongly in applied microeconomics, but econometrics has been left far behind. The first section provides a foundation for thinking about any statistical test and getting the sample right. 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.