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Mostly Harmless Econometrics
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Mostly Harmless Econometrics : An Empiricist's Companion

Book Details

Format Paperback / Softback
ISBN-10 0691120358
ISBN-13 9780691120355
Publisher Princeton University Press
Imprint Princeton University Press
Country of Manufacture US
Country of Publication GB
Publication Date Jan 4th, 2009
Print length 392 Pages
Weight 456 grams
Dimensions 21.60 x 14.20 x 2.40 cms
Product Classification: Econometrics
Ksh 8,100.00
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Shows how the basic tools of applied econometrics allow the data to speak. This book covers regression-discontinuity designs and quantile regression - as well as how to get standard errors right. It is suitable for various areas in contemporary social science.

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometrics

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 Jörn-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

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