Introduction to the advanced theory of nonparametric econometrics :
Racine, Jeffrey S.
Introduction to the advanced theory of nonparametric econometrics : a replicable approach using R Jeffrey S. Racine. - Cambridge: Cambridge University Press, 2019. - xxvi, 408p.
Includes bibliographical references and index.
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.
"An Introduction to the Advanced Theory of Nonparametric Econometrics 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"--
9781108483407 (Hbk) = ES-reference book collection
2018041299
Econometrics.
Nonparametric statistics.
R (Computer program language)
HB139 / .R3292 2019
330.0151954 R115I
Introduction to the advanced theory of nonparametric econometrics : a replicable approach using R Jeffrey S. Racine. - Cambridge: Cambridge University Press, 2019. - xxvi, 408p.
Includes bibliographical references and index.
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.
"An Introduction to the Advanced Theory of Nonparametric Econometrics 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"--
9781108483407 (Hbk) = ES-reference book collection
2018041299
Econometrics.
Nonparametric statistics.
R (Computer program language)
HB139 / .R3292 2019
330.0151954 R115I