An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
A Replicable Approach Using R

Provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research.

Jeffrey S. Racine (Author)

9781108483407, Cambridge University Press

Hardback, published 27 June 2019

434 pages, 80 b/w illus. 24 tables
26.1 x 18.4 x 2.3 cm, 1.08 kg

'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

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.

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.

Subject Areas: Econometrics [KCH], Economics [KC]