Mostly Harmless Econometrics, An Empiricist's Companion 近乎无害的经济计量学——经验主义者的指南 caj kindle 百度云 azw3 夸克云 下载 pdb pdf

A quirky and thought-provoking read for any budding econometrician. . . . Insightful and refreshing. -- James Davidson, Times Higher Education

I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation . . . an excellent guide to how to do basic regression/IV/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses. -- Diane Coyle, The Enlightened Economist Blog

The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences. -- Pavel Stoynov, Zentralblatt MATH

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D. Angrist is professor of economics at the Massachusetts Institute of Technology. Jorn-Steffen Pischke is professor of economics at the London School of Economics and Political Science.

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Anyone interested in drawing useful inferences from data on people can be said to be an applied econometrician.

(i) the intimate connection between the population regression function and the conditional expectation function

(ii) how and why regression coeffcients change as covariates are added or removed from the model

(iii) the close link between regression and other "control strategies" such as matching

(iv) the sampling distribution of regression estimates

This is the selection-on-observables assumption for regression models discussed over a quarter century ago by Barnow, Cain, and Goldberger (1981). It remains the basis of most empirical work in Economics.

Good econometrics cannot save a shaky research agenda, but the promiscuous use of fancy econometric techniques sometimes brings down a good one.

Today, we are more likely to find IV used to address measument error problems than to estimate the parameters if an SEM

The observed difference in health status, however, adds to this causal effect a term called selection bias. This term is the difference in average Y0; between those who were and those who were not hospitalized. Because the sick are more likely than the healthy to seek treatment, those who were hospitalized have worse values of Y0i, making selection bias negative in this example. The selection bias may be so large (in absolute value) that it completely masks a positive treatment effect. The goal of most empirical economic research is to overcome selection bias, and therefore to say something about the causal effect of a variable like Di.

书籍介绍

The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jrn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications