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  3. An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
  • Cited by 10
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    • Publisher:
      Cambridge University Press
      Publication date:
      April 2019
      June 2019
      ISBN:
      9781108649841
      9781108483407
      DOI:
      https://doi.org/10.1017/9781108649841
      Dimensions:
      (253 x 177 mm)
      Weight & Pages:
      1.08kg, 434 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
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    Subjects:
    Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
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    Book description

    Interest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git.

    Reviews

    ‘This book will be valuable to economists wishing to learn nonparametric methods, and to practitioners needing the details of implementation. Applied economists will find this an excellent and practical reference guide.'

    Bruce E. Hansen - University of Wisconsin, Madison

    ‘This book manages to be comprehensive, careful, and accessible all at once - an impressive achievement for such a challenging subject. It covers topics not found elsewhere and incorporates them in a systematic, unified approach. Illustrations using the R programming language will have broad appeal for both teachers and users of nonparametric methods.'

    Jeffrey M. Woolridge - Michigan State University

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