{ "_id": "6a93e3f07b325a6d97b710f6", "Package": "dfms", "Version": "1.0.1", "Title": "Dynamic Factor Models", "Authors@R": "c(person(\"Sebastian\", \"Krantz\", role = c(\"aut\", \"cre\"), email = \"sebastian.krantz@graduateinstitute.ch\"),\nperson(\"Rytis\", \"Bagdziunas\", role = \"aut\"),\nperson(\"Santtu\", \"Tikka\", role = \"rev\"),\nperson(\"Eli\", \"Holmes\", role = \"rev\"),\nperson(\"Christian\", \"Klettner\", role = \"ctb\"))", "Description": "Efficient estimation of Dynamic Factor Models using the\nExpectation Maximization (EM) algorithm or Two-Step (2S)\nestimation, supporting datasets with missing data and\nmixed-frequency nowcasting applications. Factors follow a\nstationary VAR process of order p. Estimation options include:\nrunning the Kalman Filter and Smoother once with PCA initial\nvalues (2S) as in Doz, Giannone and Reichlin (2011)\n; iterated Kalman Filtering\nand Smoothing until EM convergence as in Doz, Giannone and\nReichlin (2012) ; or the adapted EM\nalgorithm of Banbura and Modugno (2014) ,\nallowing arbitrary missing-data patterns and monthly-quarterly\nmixed-frequency datasets. The implementation uses the\n'Armadillo' 'C++' library and the 'collapse' package for fast\nestimation. A comprehensive set of methods supports\ninterpretation and visualization, forecasting, and\ndecomposition of the 'news' content of macroeconomic data\nreleases following Banbura and Modugno (2014). 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