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Applied nonparametric econometrics / Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami

By: Contributor(s): Publisher: New York : Cambridge University Press, 2015Description: xii, 367 pages ; 27 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781107010253 Hardback
  • 9780521279680 Paperback
Subject(s): DDC classification:
  • 330.0151954 .H496
Summary: "The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses - in terms that someone with one year of graduate econometrics can understand - basic to advanced nonparametric methods. The analysis starts with density estimation and moves through familiar methods and on to kernel regression, estimation with discrete data and advanced methods such as estimation with panel data and instrumental variables models. The book addresses issues that arise with programming, computing speed and application. In each chapter, the methods are applied to actual data, paying attention to presentation of results and potential pitfalls." -- Provided by publisher.
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Print Materials Graduate School Library Master in Public Administration 330.0151954 .H496 2015 (Browse shelf(Opens below)) Available 0116296

Includes bibliographical references (pages 343-358) and index

"The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses - in terms that someone with one year of graduate econometrics can understand - basic to advanced nonparametric methods. The analysis starts with density estimation and moves through familiar methods and on to kernel regression, estimation with discrete data and advanced methods such as estimation with panel data and instrumental variables models. The book addresses issues that arise with programming, computing speed and application. In each chapter, the methods are applied to actual data, paying attention to presentation of results and potential pitfalls." -- Provided by publisher.

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Introduction

Text in English

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