Mastering 'Metrics: The Path from Cause to Effect

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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. The people they asked to provide blurbs for the back include Hal Varian (of Google), and Andrew Gelman (of Bayesian and Stan fame).

Posing several well-chosen empirical questions in social science, Mastering 'Metrics develops methods to provide the answers and applies them to interesting datasets.

Becker, WE, and WH Greene (2001), “Teaching Statistics and Econometrics to Undergraduates”, Journal of Economic Perspectives, 169-182. It demonstrates how econometric techniques can be used to arrive at the critical point of “other things being equal” despite such key obstacles as selection and omitted-variables bias. Are expensive private colleges and selective public high schools better than more pedestrian institutions?

They certainly bring in a lot of that - where they talk about apples-to-apples comparison; but the emphasis is not approached as a general method of empirical analysis and the book can go far if that is emphasized.It follows and complements a recent Journal of Econometrics special issue also in honor of William A. The first empirical application in Hill, Griffiths, and Lim (2011: 49) explores the correlation between food expenditure and income. Modern econometrics is more than just a set of statistical tools--causal inference in the social sciences requires a careful, inquisitive mindset.

Steve began by considering some of the key questions that we might like to answer by using econometrics - from the benefits of going to university to the impact of monetary policy on the economy. I still don’t understand things immediately through formulas, so coupling those with summaries at the end of each chapter was great.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. In the fourth section, the authors cover how breaks and discontinuities offer opportunities for uncovering useful information. Posing several well-chosen empirical questions in social science, "Mastering 'Metrics" develops methods to provide the answers and applies them to interesting datasets. There is nothing wrong with having an accessible primer on the intuition behind an idea, but if you're doing that you should probably be upfront about it and make recommendations on where to go from here. Measuring acceleration or movement away from core tendencies or trends is essential for the analysis of most data.

We explain why these questions are challenging and why simple empirical strategies to address them can be misleading. Understanding the dependencies within the data will strongly influence the choice of strategies for devising useful models.Twins are used to examine schooling outcomes as they help keep ability fixed - identical twins are said to be similarly abled. 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. The insured tend to be older, better educated, have slightly smaller families, are more likely to be employed and have higher family incomes.