Racine Jeffrey S., Professor, Department of Economics | Professor, Graduate Program in Statistics, Department of Mathematics and Statistics | Senator William McMaster Chair in Econometrics | Fellow of Journal of Econometrics | Associate Editor, Econometric Reviews
Jeffrey S. Racine
Professor, Department of Economics | Professor, Graduate Program in Statistics, Department of Mathematics and Statistics | Senator William McMaster Chair in Econometrics | Fellow of Journal of Econometrics | Associate Editor, Econometric Reviews
Faculty
Department of Economics
Area(s) of Interest:
Biography
Jeffrey S. Racine (Ph.D. University of Western Ontario, 1989, Aman Ullah, Supervisor) is a Professor in the Department of Economics and a Professor in the Graduate Program in Statistics in the Department of Mathematics and Statistics at McMaster University. He occupies the Senator William McMaster Chair in Econometrics and is a Fellow of the Journal of Econometrics. He has held previous appointments at Syracuse University, the University of South Florida, the University of California San Diego (two-year visiting appointment), and York University.
His research interests include nonparametric estimation and inference, shape constrained estimation, cross-validatory model selection, frequentist model averaging, nonparametric instrumental methods, and entropy-based measures of dependence and their statistical underpinnings. He is also interested in parallel distributed computing paradigms and their application to computationally intensive nonparametric estimators.
He is currently serving as an Associate Editor for the journal Econometric Reviews.
He has co-authored the textbook Nonparametric Econometrics: Theory and Practice (joint with Qi Li, published by Princeton University Press, 2007, with a Chinese translation published in 2015), the monograph Nonparametric Econometrics: A Primer (published by Foundations and Trends in Econometrics, 2008, with a Russian translation published in the journal Quantile in 2008), the textbook Reproducible Econometrics Using R (published by Oxford University Press, 2018), and the textbook An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics: A Replicable Approach Using R (published by Cambridge University Press, 2019). He has published extensively in peer reviewed journals in his field and has co-authored the R packages np and crs that are available on the Comprehensive R Archive Network (CRAN).
Education
Ph.D. University of Western Ontario 1989 (Aman Ullah, Supervisor)
M.A. McMaster University 1985
B.A. McMaster University 1984 (Summa Cum Laude)
Teaching
Fall 2023
- Econ 2B03
- Econ 768
Reference Letters
Occasionally I am asked to provide a letter of reference on behalf of a student. In order for me to consent to such requests, you must have taken a course with me, you must have received a grade of A- or higher in the course, and you must have an overall average of B+ or higher (no exceptions will be made). Kindly appreciate that no letter of reference is better than a weak letter of reference. If you did not receive at least an A- in my course and have not maintained an overall average of B+ or higher then I simply cannot in good conscience write a strong letter of recommendation on your behalf.
Research
Books
1. Racine, J.S. (2019), An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics (A Replicable Approach Using R), Cambridge University Press, ISBN 9781108483407, 408 pages.
Note that lecture slides, assignments, exams, and a solutions manual are available upon request to instructors who adopt this book (slides in LaTeX Beamer format). See the website link below for further details (see the Companion Website in the link below).
You can order the book directly from Cambridge University Press (Link: An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics) or from your favourite online retailer when available.
2. Racine, J.S. (2019), Reproducible Econometrics Using R, Oxford University Press, ISBN: 9780190900663, 293 pages.
Here is the Errata (pdf). Note that lecture slides, assignments, exams, and a solutions manual are available upon request to instructors who adopt this book (slides in LaTeX Beamer format). See the website link below for further details (see the Companion Website in the link below).
You can order the book directly from Oxford University Press (Link: Reproducible Econometrics Using R) or from your favourite online retailer.
3. Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press, ISBN: 9780691121611, 746 Pages.
Chinese Translation:
Li, Q. and J.S. Racine, Nonparametric Econometrics: Theory and Practice, Translated by Ye Zhong, Wu Xianbgo et al., Peking University Press (2015), ISBN: 9787301249673.
Here is the table of contents (pdf), Chapter 1 (pdf), the Errata (pdf), the solution manual containing code and answers to odd numbered questions (pdf), and R code for answers to all applied questions (zip). A solution manual containing code and answers to all questions (odd and even) is available to instructors upon request. To receive a copy kindly email me your course syllabus along with your surface mailing address. A hard copy will then be sent via surface mail.
You can order the book directly from Princeton University Press (Link: Nonparametric Econometrics: Theory and Practice) or from your favourite online retailer.
Monographs
Racine, J.S. (2008), Nonparametric Econometrics: A Primer, Foundations and Trends in Econometrics: Vol. 3: No 1, pp 1-88. (Link: http://dx.doi.org/10.1561/0800000009).
Russian Translation:
An edited version of this monograph is reprinted in Russian and appears as Racine, J.S. (2008) "Nonparametric Econometrics: A Primer", Quantile, Number 4, pp 7-56.
Here is the R code to replicate examples in this primer (zip).
Edited Volumes
Oxford Handbook of Semiparametric and Nonparametric Econometric Methods, ISBN 978–0–19–985794–4, Edited By Jeffrey S. Racine, Liangjun Su, and Aman Ullah, Published: 2014.
Advances In Econometrics: Nonparametric Econometric Methods, Volume 25, ISBN: 978-1-84950-623-6, Edited by: Qi Li, Jeffrey S. Racine, Published: 2009.
Gallery of Code and Applications for the np, npRmpi, and crs R Packages
The following link (link to gallery) will take you to a gallery where you can find some commented examples of working code for a range of estimators contained in the np, npRmpi, and crs packages outlined below. Feel free to email me with suggestions. I welcome code/examples that can be showcased and shared with other users, so please feel free to send me code that you would like to share and I will host it in the gallery along with your contact information.
The R np and npRmpi Packages
The R (www.r-project.org) np and npRmpi packages (current version 0.60-8) implement a variety of nonparametric and semiparametric kernel-based methods in R, an open source platform for statistical computing and graphics. Methods include kernel regression, kernel density estimation, kernel conditional density estimation, and a range of inferential procedures. See the links to the vignettes below for an overview of both packages (I would advise starting with the np vignette).
The np package is the standard package you would use under R, while the npRmpi package is a version that uses the message passing interface for parallel computing. The npRmpi package is designed for executing batch programs on compute clusters and multi-core computing environments to reduce the run time for computationally intensive jobs. See the example files in the demo directory of the npRmpi package for illustrative npRmpi code, and see the examples in the help files and the link for replicating examples for the primer above for code to generate a range of illustrative examples.
Here is a direct link to the np package on the Comprehensive R Archive Network (CRAN), a direct link to the npRmpi package on CRAN, a direct link to the CHANGELOG file on CRAN (documents differences between all versions), an npRmpi test file `test.R' (text), the npRmpi .Rprofile file (text), install instructions for npRmpi under Windows (text), and instructions for compiling the npRmpi binary from scratch under Windows (text), and instructions for compiling the npRmpi source from scratch for Mac OS X Mountain Lion (text). See the npRmpi github repository (link below) for a recent npRmpi MS Windows binary (available as a binary zip file from the github Downloads menu) and a recent npRmpi Mac OS X binary (available as a binary tgz file from the github Downloads menu).
See the October 2007 Rnews article (pdf) that describes the np package, the np vignette (pdf) for an overview of the np package, the npRmpi vignette (pdf) for an overview of the installation and use of the npRmpi package, and the entropy-based inference vignette for an overview of computing entropy measures (pdf) (R code).
See also the review of the np package that appeared in 2008 in the Journal of Applied Econometrics (link to article in the Wiley Online Library) and the review of the npRmpi package that appeared in 2011 in the Journal of Applied Econometrics (link to article in the Wiley Online Library).
These packages are hosted on github (link)
The R crs Package
The R (www.r-project.org) crs package (current version 0.15-31) implements multivariate regression splines (and quantile regression splines as of version 0.15-8) with both continuous and categorical predictors in R, an open source platform for statistical computing and graphics. See the links to the vignettes below for an overview of the package.
See the R Journal article (pdf) that describes the crs package, the crs vignette (pdf) for an overview of the crs package and the spline primer vignette for an overview of regression splines (pdf).
This package is hosted on github (link).
The R ma Package
The R (www.r-project.org) ma package (current version 1.0-8) implements model averaging using a variety of multivariate bases and averaging criteria.
The R (www.r-project.org) ma package (current version 1.0-1) implements the Hansen-Racine bootstrap model average unit root test