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Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)

Author

Listed:
  • Ferwerda, Jeremy
  • Hainmueller, Jens
  • Hazlett, Chad J.

Abstract

The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without strong functional form assumptions or a specification search. The flexible KRLS estimator learns the functional form from the data, thereby protecting inferences against misspecification bias. Yet it nevertheless allows for interpretability and inference in ways similar to ordinary regression models. In particular, KRLS provides closed-form estimates for the predicted values, variances, and the pointwise partial derivatives that characterize the marginal effects of each independent variable at each data point in the covariate space. The method is thus a convenient and powerful alternative to ordinary least squares and other generalized linear models for regression-based analyses.

Suggested Citation

  • Ferwerda, Jeremy & Hainmueller, Jens & Hazlett, Chad J., 2017. "Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i03).
    • Handle: RePEc:jss:jstsof:v:079:i03
    DOI: http://hdl.handle.net/10.18637/jss.v079.i03
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    References listed on IDEAS

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    1. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    2. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    3. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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