- Ph.D. University of Western Ontario 1989
Curriculum Vitae (pdf)
Research Interests: nonparametric estimation and inference, cross-validatory model selection, 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.
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 (press.princeton.edu/titles/8355.html) or from your favourite online retailer.
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).
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 0.50-1) 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 0.15-22) 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.
Gao, Q. and L. Liu and J.S. Racine (forthcoming), "A Partially Linear Kernel Estimator for Categorical Data," Econometric Reviews.
Racine, J.S. (forthcoming), "Mixed Data Kernel Copulas," Empirical Economics.
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 http://tandfonline.com/r/JBES).
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.
Li, Q. and J.S. Racine (2010), "Smooth Varying-Coefficient Estimation and Inference for Qualitative and Quantitative Data," Econometric Theory, Volume 26, 1607-1637.
Gyimah-Brempong, K. and J.S. Racine (2010), "Aid and Economic Development: A Robust Approach," Journal of International Trade & Economic Development, Volume 19, 319-349.
Racine, J.S., (2009), "Nonparametric and Semiparametric Methods in R," Advances in Econometrics: Nonparametric Econometric Methods, Elsevier Science, Volume 25, 335-375.
Li, C., D. Ouyang and J.S. Racine (2009), "Nonparametric Regression with Weakly Dependent Data: The Discrete and Continuous Regressor Case," Journal of Nonparametric Statistics. Volume 21, Number 6, pp. 697-711.
Kiefer, N.M. and J.S. Racine (2009), "The Smooth Colonel Meets the Reverend," Journal of Nonparametric Statistics, Volume 21, Issue 5, pp 521-533.
Li, Q., J.S. Racine and J. M. Woodridge (2009), "Efficient Estimation of Average Treatment Effects With Mixed Categorical and Continuous Data,'' Journal of Business and Economic Statistics, Volume 27, Number 2, pp 206-223.
Meredith, E. and J.S. Racine (2009), “Towards Reproducible Econometric Research: The Sweave Framework,” Journal of Applied Econometrics, Volume 24, pp 366-374.
Li, Q., E. Maasoumi and J.S. Racine (2009), "A Nonparametric Test for Equality of Distributions with Mixed Categorical and Continuous Data," Journal of Econometrics, Volume 148, Issue 2, pp. 186-200.
Ouyang, D., Q. Li and J.S. Racine (2009), "Nonparametric Estimation of Regression Functions with Discrete Regressors," Econometric Theory, Volume 25, Issue 01, pp 1-42.
Maasoumi, E. and J.S. Racine (2009), "A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes," Econometric Reviews, Volume 28, pp 246 - 261.
Li, Q. and J.S. Racine (2008), "Nonparametric Estimation of Conditional CDF and Quantile Functions with Mixed Categorical and Continuous Data," Journal of Business and Economic Statistics, Volume 26, Number 4, pp. 423-434.
Hayfield, T. and J.S. Racine (2008), "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Volume 27, Number 5, pp 1-32, http://www.jstatsoft.org/v27/i05/ .
Li, J. and J.S. Racine (2008), "Maxima: An Open Source Computer Algebra System," Journal of Applied Econometrics, Volume 23, Issue 4, pp 515-523.
Racine, J.S. (2008) "Nonparametric Econometrics: A Primer," Foundations and Trends in Econometrics, Volume 3, Number 1, pp 1-88.
Li, Q., J.S. Racine and J. Wooldridge (2008), "Estimating Average Treatment Effects with Continuous and Discrete Covariates: The Case of Swan-Ganz Catherization," American Economic Review, Volume 98, Number 2, pp. 357-62.
Working Papers, Citation Summary, and Miscellany