Skip to content
Open global navigation

Cambridge University Press

AcademicLocation selectorSearch toggleMain navigation toggle
Cart
Register Sign in Wishlist
Semiparametric Regression for the Applied Econometrician

Semiparametric Regression for the Applied Econometrician

$39.99

Part of Themes in Modern Econometrics

  • Date Published: June 2003
  • availability: Available
  • format: Paperback
  • isbn: 9780521012263

$39.99
Paperback

Add to cart Add to wishlist

Other available formats:
Hardback, eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • Adonis Yatchew provides simple and flexible (nonparametric) techniques for analyzing regression data. He includes a series of empirical examples with the estimation of Engel curves and equivalence scales, scale economies, household gasoline consumption, housing prices, option prices and state price density estimation. The book is of interest to a broad range of economists including those working in industrial organization, labor, development, and urban, energy and financial economics.

    • Offers practical nonparametric and semiparametric techniques for applied practitioners, filling a real gap in this advanced literature
    • Numerous empirical examples provided
    • Data and code (in S-Plus) will also appeal to practitioners
    Read more

    Reviews & endorsements

    "This outstanding textbook transforms abstract theoretical developments in nonparametric and semiparametric regression models into insightful and illuminating applications of great interest to empirical econometricians. The examples and exercises are well-crafted, and data and software code are accessible via the Internet."
    Ernst Berndt, MIT

    "Yatchew has written an exceptionally clear and accessible book. Full of applications to household consumption data, it makes an invaluable text and reference for the applied researcher."
    Richard Blundell, University College, London

    "An invaluable resource for the applied econometrician who wants to learn how to do applied nonparametric and semiparametric empirical studies. The many empirical examples as well as the theoretical development demonstrate a deep understanding of the topics covered."
    Jerry Hausman, MIT

    "This fluent book is an excellent source for learning, or updating oneas knowledge of semi- and nonparametric methods and their applications. It is a valuable addition to the existent books on these topics."
    Rosa Matzkin, Northwestern University

    "Yatchew's book is an excellent account of semiparametric regression. The material is nicely integrated by using a simple set of ideas which exploit the impact of differencing and weighting operations on the data. The empirical applications are attractive and will be extremely helpful for those encountering this material for the first time."
    Adrian Pagan, Australian National University

    "At the University of Toronto Adonis Yatchew is known for excellence in teaching. The key to this excellence is the succinct transparency of his exposition. At its best such exposition transcends the medium of presentation (either lecture or text). This monograph reflects the clarity of the authoras thinking on the rapidly expanding fields of semiparametric and nonparametric analysis. Both students and researchers will appreciate the mix of theory and empirical application."
    Dale Poirier, University of California, Irvine

    "A concise self-contained treatment of nonparametric and seminonparametric regression models and their applications in econometrics. It is written in an accessible style and provides key intuition for nonparametric and seminonparametric regression methods in a cross-sectional, essentially independently distributed, setting. Well-explained theoretical ideas are illustrated by many real-data examples and exercises, mainly from the field of applied microeconometrics."
    Fabio Trojani, University of St. Gallen, Journal of the American Statistician

    See more reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: June 2003
    • format: Paperback
    • isbn: 9780521012263
    • length: 236 pages
    • dimensions: 229 x 152 x 14 mm
    • weight: 0.35kg
    • contains: 30 b/w illus. 22 tables
    • availability: Available
  • Table of Contents

    List of figures and tables
    Preface
    1. Introduction to differencing
    2. Background and overview
    3. Introduction to smoothing
    4. Higher-order differencing procedures
    5. Nonparametric functions of several variables
    6. Constrained estimation and hypothesis testing
    7. Index models and other semiparametric specifications
    8. Bootstrap procedures
    Appendixes
    References
    Index.

  • Author

    Adonis Yatchew, University of Toronto

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

You are now leaving the Cambridge University Press website, your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Find content that relates to you

© Cambridge University Press 2014

Back to top

Are you sure you want to delete your account?

This cannot be undone.

Cancel Delete

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×