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Jeffrey S. Racine

Jeffrey S. Racine

Senator William McMaster Chair in Econometrics; Professor, Department of Economics; Professor, Graduate Program in Statistics, Department of Mathematics and Statistics

Department of Economics
Kenneth Taylor Hall, Rm 431
McMaster University
1280 Main Street West
Hamilton, Ontario, L8S 4M4

Office Phone: 905-525-9140 ext. 23825


  1. Ph.D. University of Western Ontario 1989


Curriculum Vitae (pdf)

On Research Leave Jan 1 - Dec 31 2015

Research Interests: nonparametric estimation and inference, cross-validatory model selection, frequentist model averaging, nonparametric instrumental methods, and entropy-based measures of dependence and their statistical underpinnings. I am also interested in parallel distributed computing paradigms and their application to computationally intensive nonparametric estimators.


Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press, ISBN: 9780691121611, 768 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 ( or from your favourite online retailer.


Racine, J.S. (2008), Nonparametric Econometrics: A Primer,  Foundations and Trends in Econometrics: Vol. 3: No 1, pp 1-88.

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

Consult the np FAQ (pdf) for responses to commonly asked questions and the user manual (pdf) for functions, descriptions, and examples.

The R ( np and npRmpi packages (current version 0.60-2) 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

Consult the crs FAQ (pdf) for responses to commonly asked questions and the user manual (pdf) for functions, descriptions, and examples.

The R ( crs package (current version 0.15-23) 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.

Here is a direct link to the crs package on the Comprehensive R Archive Network (CRAN), a direct link to the CHANGELOG file on CRAN (documents differences between all versions).

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).

Recent Research Papers

Maasoumi, E. and J.S. Racine (forthcoming), "A Solution to Aggregation and an Application to Multidimensional `Well-Being' Frontiers," Journal of Econometrics.

Chakrabarty, M. and A. Majumder and J.S. Racine (forthcoming), "Household Preference Distribution and Welfare Implication: An Application of Multivariate Distributional Statistics," Journal of Applied Statistics.

Ma, S. and J.S. Racine and L. Yang (forthcoming), "Spline Regression in the Presence of Categorical Predictors," Journal of Applied Econometrics.

Kiefer, N.M. and J.S. Racine (forthcoming), "The Smooth Colonel and the Reverend Find Common Ground," Econometric Reviews.

Hall, P. and J.S. Racine (2015), "Infinite-Order Cross-Validated Local Polynomial Regression," Journal of Econometrics, Volume 185, 510-525.

Racine, J.S. (2015), "Mixed Data Kernel Copulas," Empirical Economics, Volume 48, 37-59.

Gao, Q. and L. Liu and J.S. Racine (2015), "A Partially Linear Kernel Estimator for Categorical Data," Econometric Reviews, 34 (6-10), 958-977.

Racine, J.S. and C. Parmeter (2014), "Data-Driven Model Evaluation: A Test for Revealed Performance," in `Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics', Oxford University Press, (A. Ullah, J.S. Racine and L. Su Eds), 308-345.

Du, P. and C. Parmeter and J.S. Racine (2013), "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Statistica Sinica, Volume 23, 1343-1372.

Li, C. and J.S. Racine (2013), "A Smooth Nonparametric Conditional Density Test for Categorical Responses," Econometric Theory, Volume 29, 629-641.

Li, Q. and D. Ouyang and J.S. Racine (2013), "Categorical Semiparametric Varying-Coefficient Models," Journal of Applied Econometrics, Volume 28, 551-579.

Ma, S. and J.S. Racine (2013), "Additive Regression Splines with Irrelevant Categorical and Continuous Regressors," Statistica Sinica, Volume 23, 515-541.

Li, Q. and J. Lin and J.S. Racine (2013), "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business and Economic Statistics, Volume 31, 57-65 (19 pages of supplementary material [proofs] available online at

Nie, Z. and J.S. Racine (2012), "The crs Package: Nonparametric Regression Splines for Continuous and Categorical Predictors," The R Journal, Volume 4, 48-56.

Parmeter, C. and J.S. Racine (2012), "Smooth Constrained Frontier Analysis," in `Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White, Jr.', Springer Verlag, (X. Chen and N.R. Swanson Eds), 463-488.

Racine, J.S. (2012), "RStudio: A Platform Independent IDE for R and Sweave," Journal of Applied Econometrics, Volume 27, 167-172.

Hansen, B. and J.S. Racine (2012), "Jackknife Model Averaging," Journal of Econometrics, Volume 167, 38-46.

Gyimah-Brempong, K. and J.S. Racine and A. Gyapong (2012), "Aid and Economic Growth: Sensitivity Analysis," Journal of International Development, Volume 24, 17-33. 

Zhang, Z., D. Chen, W. Liu, J.S. Racine, S. Ong, Y. Chen, G. Zhao and Q. Ziang (2011), “Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach,” PLoS ONE, Volume 6, Number 3, e17381, pages 1–8.

Racine, J.S. (2011), "Nonparametric Kernel Methods for Qualitative and Quantitative Data," The Handbook of Empirical Economics and Finance, CRC Press, 183-204.

Working Papers, Citation Summary, and Miscellany

Working Papers and Articles (RePEc Author Service)

Citation Counts and Related Indices (Google Scholar Citations)

Top 10% Institutions and Economists in the Field of Econometrics (RePEc)

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