000			02854nam a22002057a 4500
999			_c2979 _d2979
005			20220813101825.0
008			220715b ||||| |||| 00| 0 eng d
020			_a9781108483407
082			_a330.0151954 _bRAC
100			_aRacine, Jeffrey S. _96853
245			_aAn introduction to the advanced theory of nonparametric econometrics: a replicable approach using R
260			_bCambridge University Press _aNew York _c2019
300			_axxvi, 408 p.
365			_aGBP _b38.99
504			_aTable 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.
520			_aDescriptionContentsResourcesCoursesAbout the Authors 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
650			_aNonparametric statistics _97513
650			_aR (Computer program language) _91512
942			_2ddc _cBK