An introduction to the advanced theory of nonparametric econometrics : a replicable approach using R

著者

書誌事項

An introduction to the advanced theory of nonparametric econometrics : a replicable approach using R

Jeffrey S. Racine

Cambridge University Press, 2019

タイトル別名

An introduction to the advanced theory and practice of nonparametric econometrics : a replicable approach using R

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注記

Includes bibliographical references (p. 367-390) and indexes

内容説明・目次

内容説明

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.

目次

  • Part I. Probability Functions, Probability Density Functions, and their Cumulative Counterparts: 1. Discrete probability and cumulative probability functions
  • 2. Continuous density and cumulative distribution functions
  • 3. Mixed-data probability density and cumulative distribution functions
  • 4. Conditional probability density and cumulative distribution functions
  • Part II. Conditional Moment Functions and Related Statistical Objects: 5. Conditional moment functions
  • 6. Conditional mean function estimation
  • 7. Conditional mean function estimation with endogenous predictors
  • 8. Semiparametric conditional mean function estimation
  • 9. Conditional variance function estimation
  • Part III. Appendices: A. Large and small orders of magnitude and probability
  • B. R, RStudio, TeX and Git
  • C. Computational considerations
  • D. R Markdown for assignments
  • E. Practicum.

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