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: | 0.3.2 |
| 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: | 2025-09-24 |
| 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/, https://github.com/SebKrantz/dfms |
| NeedsCompilation: | yes |
| Materials: | README, NEWS |
| In views: | TimeSeries |
| CRAN checks: | dfms results |
| Reference manual: | dfms.html , dfms.pdf |
| Vignettes: |
Introduction to dfms (source, R code) Dynamic Factor Models: A Very Short Introduction (source) |
| Package source: | dfms_0.3.2.tar.gz |
| Windows binaries: | r-devel: dfms_0.3.2.zip, r-release: dfms_0.3.2.zip, r-oldrel: dfms_0.3.2.zip |
| macOS binaries: | r-release (arm64): dfms_0.3.2.tgz, r-oldrel (arm64): dfms_0.3.2.tgz, r-release (x86_64): dfms_0.3.2.tgz, r-oldrel (x86_64): dfms_0.3.2.tgz |
| Old sources: | dfms archive |
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