Category Mostly Harmless Econometrics: An Empiricist’s Companion

Economic Relationships and the Conditional Expectation Function

Empirical economic research in our field of Labor Economics is typically concerned with the statistical analysis of individual economic circumstances, and especially differences between people that might account for differences in their economic fortunes. Such differences in economic fortune are notoriously hard to explain; they are, in a word, random. As applied econometricians, however, we believe we can summarize and interpret randomness in a useful way. An example of “systematic randomness” mentioned in the introduction is the connection between education and earnings. On average, people with more schooling earn more than people with less schooling...

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Mostly Harmless Econometrics: An Empiricist’s Companion

The universe of econometrics is constantly expanding. Econometric methods and practice have advanced greatly as a result, but the modern menu of econometric methods can seem confusing, even to an experienced number-cruncher. Luckily, not everything on the menu is equally valuable or important. Some of the more exotic items are needlessly complex and may even be harmful. On the plus side, the core methods of applied econometrics remain largely unchanged, while the interpretation of basic tools has become more nuanced and sophisticated. Our Companion is an empiricist’s guide to the econometric essentials. . . Mostly Harmless Econometrics.

The most important items in an applied econometrician’s toolkit are:


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Linear Regression and the CEF

So what’s the regression you want to run?

In our world, this question or one like it is heard almost every day. Regression estimates provide a valuable baseline for almost all empirical research because regression is tightly linked to the CEF, and the CEF

Подпись: P = argminE b Подпись: (Y. - Xjb)2 Подпись: (3.1.2)

provides a natural summary of empirical relationships. The link between regression functions – i. e., the best-fitting line generated by minimizing expected squared errors – and the CEF can be explained in at least 3 ways. To lay out these explanations precisely, it helps to be precise about the regression function we have in mind. This chapter is concerned with the vector of population regression coefficients, defined as the solution to a population least squares problem. At this point, we are not worried about causality...

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