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  3. Nonparametric Econometrics
  • Cited by 619
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    • Publisher:
      Cambridge University Press
      Publication date:
      03 December 2009
      13 June 1999
      ISBN:
      9780511612503
      9780521355643
      9780521586115
      DOI:
      https://doi.org/10.1017/CBO9780511612503
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.82kg, 444 Pages
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.59kg, 444 Pages
      • Subjects:
      Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
      Series:
      Themes in Modern Econometrics
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    • Selected: Digital
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    Subjects:
    Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
    Series:
    Themes in Modern Econometrics
    Information

    Book description

    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.

    Reviews

    ‘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.’

    Source: Short Book Reviews

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