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  "Title": "Clustered Jackknife Instrumental Variables Estimation",
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  "Authors@R": "person(\"Atal\", \"Katawazi\", email = \"atalkatawazi@hotmail.com\",\nrole = c(\"aut\", \"cre\"))",
  "Description": "Tools for instrumental variables estimation and inference\nunder clustered errors with many instruments. The current\nrelease provides the cluster-jackknife IV estimator (CJIVE) of\nFrandsen, Leslie and McIntyre (2025) <doi:10.1162/rest.a.263>\nfor a single endogenous regressor in a just-identified design,\nwith cluster-robust inference: each observation's first-stage\nvalue is fitted leaving out its entire cluster, which removes\nthe many-instrument bias that survives clustering. The\nleave-cluster-out fits use an exact Woodbury block update --\none factorisation of the instrument Gram matrix plus a small\nsolve per cluster -- so the estimator scales to large samples.\nA companion 'iv_compare()' reports ordinary least squares,\ntwo-stage least squares, the observation-level jackknife and\nCJIVE on a common cluster-robust standard error.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/atal-kat/Clustered-Estimation-and-Inference",
  "BugReports": "https://github.com/atal-kat/Clustered-Estimation-and-Inference/issues",
  "Repository": "https://atal-kat.r-universe.dev",
  "Date/Publication": "2026-06-24 15:41:25 UTC",
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