Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR process of order p. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
Version: 0.3.0 Depends: R (≥ 3.5.0) Imports: Rcpp (≥ 1.0.1), collapse (≥ 2.0.0) LinkingTo: Rcpp, RcppArmadillo Suggests: xts, vars, magrittr, testthat (≥ 3.0.0), knitr, rmarkdown, covr Published: 2025-05-18 DOI: 10.32614/CRAN.package.dfms Author: Sebastian Krantz [aut, cre], Rytis Bagdziunas [aut], Santtu Tikka [rev], Eli Holmes [rev] Maintainer: Sebastian Krantz <sebastian.krantz at graduateinstitute.ch> BugReports: https://github.com/SebKrantz/dfms/issues License: GPL-3 URL: https://sebkrantz.github.io/dfms/ NeedsCompilation: yes Materials: README NEWS In views: TimeSeries CRAN checks: dfms results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=dfms to link to this page.
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