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  "Title": "Quantile-on-Quantile Kernel Regularized Least Squares",
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  "Authors@R": "c(\nperson(\"Merwan\", \"Roudane\",\nemail = \"merwanroudane920@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Tomiwa Sunday\", \"Adebayo\", role = \"ctb\",\ncomment = \"Original QQKRLS methodology\"),\nperson(\"Jens\", \"Hainmueller\", role = \"ctb\",\ncomment = \"Original KRLS methodology\"),\nperson(\"Chad\", \"Hazlett\", role = \"ctb\",\ncomment = \"Original KRLS methodology\"))",
  "Description": "Implements Quantile-on-Quantile Kernel-Based Regularized\nLeast Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024)\n<doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based\nRegularized Least Squares (KRLS) of Hainmueller and Hazlett\n(2014) <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile\nregression of Sim and Zhou (2015)\n<doi:10.1016/j.jbankfin.2015.01.013>: for each quantile theta\nof the independent variable the response is fit by KRLS on the\ncorresponding sub-sample and the tau-quantile of the resulting\npointwise marginal effects yields beta(theta, tau). Standard\nerrors come from a paired bootstrap. Visualisations use the\n'MATLAB' 'Parula' colour map by default.",
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  "Date/Publication": "2026-06-01 18:26:32 UTC",
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