An introduction to the advanced theory of nonparametric econometrics :
Racine, Jeffrey Scott, 1962-
An introduction to the advanced theory of nonparametric econometrics : a replicable approach using R / Jeffrey S. Racine. - Cambridge : Cambridge University Press, 2019. - 1 online resource (xxvi, 408 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 26 Mar 2019).
Discrete probability and cumulative probability functions -- Continuous density and cumulative distribution functions -- Mixed-data probability density and cumulative distribution functions -- Conditional probability density and cumulative distribution functions -- Conditional moment functions -- Conditional mean function estimation -- Conditional mean function estimation with endogenous predictors -- Semiparametric conditional mean function estimation -- Conditional variance function estimation.
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.
9781108649841 (ebook)
Econometrics.
Nonparametric statistics.
R (Computer program language)
HB139 / .R3292 2019
330.01/51954
An introduction to the advanced theory of nonparametric econometrics : a replicable approach using R / Jeffrey S. Racine. - Cambridge : Cambridge University Press, 2019. - 1 online resource (xxvi, 408 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 26 Mar 2019).
Discrete probability and cumulative probability functions -- Continuous density and cumulative distribution functions -- Mixed-data probability density and cumulative distribution functions -- Conditional probability density and cumulative distribution functions -- Conditional moment functions -- Conditional mean function estimation -- Conditional mean function estimation with endogenous predictors -- Semiparametric conditional mean function estimation -- Conditional variance function estimation.
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.
9781108649841 (ebook)
Econometrics.
Nonparametric statistics.
R (Computer program language)
HB139 / .R3292 2019
330.01/51954