An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
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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.
- R code is provided for all examples and can be studied and modified by the reader
- Each chapter ends with a 'Practitioner's Corner' providing a set of commented examples in R that can be refined by the reader to suit their needs
- An online solutions manual is available to instructors along with LaTeX beamer PDF formatted slides authored in R Markdown that can be modified and tailored to an instructor's needs
Reviews & endorsements
‘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
Product details
- Published: June 2019
- Format: Adobe eBook Reader
- ISBN: 9781108757287
- Length: 0 pages
- Contains: 80 b/w illus. 24 tables
- Availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- 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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