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Nonparametric Econometrics

Nonparametric Econometrics


Part of Themes in Modern Econometrics

  • Date Published: August 1999
  • availability: Available
  • format: Paperback
  • isbn: 9780521586115

£ 37.99

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About the Authors
  • This book systematically and thoroughly covers the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades. Within this framework this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g. regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Nonparametric and semiparametric methods potentially offer considerable reward to applied researchers, owing to the methods' ability to adapt to many unknown features of the data. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of the modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.

    • Most up-to-date and integrated coverage of subject available
    • Adrian Pagan is a world-class econometrician, well known for work in economic theory and public policy as well
    • Fills major gap in literature; will be a basic text for advanced first year graduate students in econometrics
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    Reviews & endorsements

    'The authors of this well-produced volume merit high praise for their endeavours. This will be the most comprehensive summary of nonparametric statistics that we are likely to see for a long time. I can recommend it as a guide to recent work in an important area of mathematical statistics.' Short Book Reviews

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    Product details

    • Date Published: August 1999
    • format: Paperback
    • isbn: 9780521586115
    • length: 444 pages
    • dimensions: 229 x 152 x 25 mm
    • weight: 0.65kg
    • contains: 17 b/w illus. 8 tables
    • availability: Available
  • Table of Contents

    1. Introduction
    2. Methods of density estimation
    3. Conditional moment estimation
    4. Nonparametric estimation of derivatives
    5. Semiparametric estimation of single equation models
    6. Semi and nonparametric estimation of simultaneous equation models
    7. Semiparametric estimation of discrete choice models
    8. Semiparametric estimation of selectivity models
    9. Semiparametric estimation of censored regression models
    10. Retrospect and prospect.

  • Authors

    Adrian Pagan, Australian National University, Canberra

    Aman Ullah, University of California, Riverside

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