KRLS: Stata module to perform Kernel–Based Regularized Least Squares
Jeremy Ferwerda, Jens Hainmueller, Chad J. Hazlett · RePEc: Research Papers in Economics · 2013
krls implements Kernel-Based Regularized Least Squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2013) that allows users to solve regression and classification problems without manual specification search and strong functional form assumptions. The flexible KRLS estimator learns the functional form from the data and thereby protects 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. KRLS is thus a convenient and powerful alternative for problems requiring regression-based analyses. For more information see Jeremy Ferwerda, Jens Hainmueller, Chad J. Hazlett (2017). Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls) Journal of Statistical Software, 79(3), 1-26. doi:10.18637/jss.v079.i03