{ "_id": "6a2e51d33efcd9bda4309273", "Package": "wARMASVp", "Type": "Package", "Title": "Winsorized ARMA Estimation for Higher-Order Stochastic\nVolatility Models", "Version": "0.2.0", "Authors@R": "c(\nperson(\"Gabriel\", \"Rodriguez-Rondon\",\nemail = \"gabriel.rodriguezrondon@mail.mcgill.ca\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0005-3769-9921\")),\nperson(\"Md. Nazmul\", \"Ahsan\", role = \"aut\"),\nperson(\"Jean-Marie\", \"Dufour\", role = \"aut\"))", "Description": "Estimation, simulation, hypothesis testing, AR-order\nselection, and forecasting for univariate higher-order\nstochastic volatility SV(p) models. Supports Gaussian,\nStudent-t, and Generalized Error Distribution (GED)\ninnovations, with optional leverage effects. Estimation uses\nclosed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based methods\nthat avoid numerical optimization. Hypothesis testing includes\nLocal Monte Carlo (LMC) and Maximized Monte Carlo (MMC)\nprocedures for leverage effects, heavy tails, and\nautoregressive order. AR-order selection is also available via\ninformation criteria (BIC/AIC) using the Kalman-filter\nquasi-likelihood and the Hannan-Rissanen ARMA residual\nvariance. Forecasting is based on Kalman filtering and\nsmoothing. See Ahsan and Dufour (2021)\n, Ahsan, Dufour, and\nRodriguez-Rondon (2025) , and Ahsan,\nDufour, and Rodriguez-Rondon (2026) \nfor details.", "License": "GPL (>= 3)", "URL": "https://github.com/roga11/wARMASVp", "BugReports": "https://github.com/roga11/wARMASVp/issues", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-14 06:57:02 UTC", "User": "root" }, "Author": "Gabriel Rodriguez-Rondon [aut, cre] (ORCID:\n), Md. 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