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

by Jeffrey S. Racine

Overview

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.

Product Details

ISBN-13:	9781108483407
Publisher:	Cambridge University Press
Publication date:	06/27/2019
Pages:	434
Product dimensions:	7.24(w) x 10.28(h) x 0.91(d)

About the Author

Jeffrey S. Racine is Professor in the Department of Economics and Professor in the Graduate Program in Statistics in the Department of Mathematics and Statistics at McMaster University, Ontario. He holds the Senator William McMaster Chair in Econometrics and is a Fellow of the Journal of Econometrics. He is co-author of Nonparametric Econometrics: Theory and Practice (2007). He has published extensively in his field and has co-authored the R packages np and crs that are available on the Comprehensive R Archive Network (CRAN).

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.

Editorial 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

From the Publisher