Jeffrey S. Racine (Ontario McMaster University)
An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
A Replicable Approach Using R
59,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
Melden Sie sich
hier
hier
für den Produktalarm an, um über die Verfügbarkeit des Produkts informiert zu werden.
Jeffrey S. Racine (Ontario McMaster University)
An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics
A Replicable Approach Using R
- Gebundenes Buch
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.
Andere Kunden interessierten sich auch für
- Herman J. Bierens (Pennsylvania State University)Introduction to the Mathematical and Statistical Foundations of Econometrics141,99 €
- Qi LiNonparametric Econometrics115,99 €
- Christopher Dougherty (Associate Professor in Economics at the LondIntroduction to Econometrics81,99 €
- Klaas SijtsmaIntroduction to Nonparametric Item Response Theory86,99 €
- Ping ZongThe Art and Science of Econometrics123,99 €
- Christian GourierouxThe Econometrics of Individual Risk44,99 €
- Subhashis Ghosal (North Carolina State University)Fundamentals of Nonparametric Bayesian Inference82,99 €
-
-
-
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.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 434
- Erscheinungstermin: 27. Juni 2019
- Englisch
- Abmessung: 261mm x 185mm x 25mm
- Gewicht: 1078g
- ISBN-13: 9781108483407
- ISBN-10: 1108483402
- Artikelnr.: 54428605
- Verlag: Cambridge University Press
- Seitenzahl: 434
- Erscheinungstermin: 27. Juni 2019
- Englisch
- Abmessung: 261mm x 185mm x 25mm
- Gewicht: 1078g
- ISBN-13: 9781108483407
- ISBN-10: 1108483402
- Artikelnr.: 54428605
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).
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.
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.
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.