This book provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research. Advanced undergraduate students, graduate students, and faculty wishing to keep abreast of this field will find this resource more accessible than similar books.
This book provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research. Advanced undergraduate students, graduate students, and faculty wishing to keep abreast of this field will find this resource more accessible than similar books.
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).
Inhaltsangabe
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.
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.
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