An introduction to the advanced theory of nonparametric econometrics: a replicable approach using R
Material type:
TextPublication details: New York Cambridge University Press 2019 Description: xxvi, 408 pISBN: 9781108483407Subject(s): Nonparametric statistics | R (Computer program language)DDC classification: 330.0151954 Summary: DescriptionContentsResourcesCoursesAbout 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
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Book
|
Indian Institute of Management LRC General Stacks | Public Policy & General Management | 330.0151954 RAC (Browse shelf(Opens below)) | 1 | Available | 002759 |
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.
DescriptionContentsResourcesCoursesAbout 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
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