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  "Title": "A Robust and Powerful Test of Abnormal Stock Returns in\nLong-Horizon Event Studies",
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  "Date": "2021-02-22",
  "Authors@R": "c(person(given = \"Siegfried\", family = \"Köstlmeier\", role = c(\"aut\", \"cre\"), email = \"siegfried.koestlmeier@gmail.com\", comment = c(ORCID = \"0000-0002-7221-6981\")), person(given = \"Seppo\", family = \"Pynnonen\", role = c(\"aut\"), email = \"sjp@uwasa.fi\"))",
  "Maintainer": "Siegfried Köstlmeier <siegfried.koestlmeier@gmail.com>",
  "Description": "Based on Dutta et al. (2018)\n<doi:10.1016/j.jempfin.2018.02.004>, this package provides\ntheir standardized test for abnormal returns in long-horizon\nevent studies. The methods used improve the major weaknesses of\nsize, power, and robustness of long-run statistical tests\ndescribed in Kothari/Warner (2007)\n<doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are\nweighted by their statistical precision (i.e., standard\ndeviation), resulting in abnormal standardized returns. This\nprocedure efficiently captures the heteroskedasticity problem.\nClustering techniques following Cameron et al. (2011)\n<doi:10.1198/jbes.2010.07136> are adopted for computing\ncross-sectional correlation robust standard errors. The\nstatistical tests in this package therefore accounts for\npotential biases arising from returns' cross-sectional\ncorrelation, autocorrelation, and volatility clustering without\npower loss.",
  "License": "BSD_3_clause + file LICENSE",
  "URL": "https://github.com/skoestlmeier/crseEventStudy",
  "Encoding": "UTF-8",
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  "Repository": "https://skoestlmeier.r-universe.dev",
  "Date/Publication": "2022-02-22 13:22:28 UTC",
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  "Author": "Siegfried Köstlmeier [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7221-6981>),\nSeppo Pynnonen [aut]",
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