dfms: Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The implementation follows advances in the econometric literature: estimation can be done either by running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation following 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: 0.1.3
Depends: R (≥ 3.0.0)
Imports: Rcpp (≥ 1.0.1), collapse (≥ 1.8.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: xts, vars, magrittr, testthat (≥ 3.0.0), knitr, rmarkdown, covr
Published: 2022-10-12
Author: Sebastian Krantz [aut, cre], Rytis Bagdziunas [aut]
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:

Reference manual: dfms.pdf
Vignettes: Introduction to dfms

Downloads:

Package source: dfms_0.1.3.tar.gz
Windows binaries: r-devel: dfms_0.1.3.zip, r-release: dfms_0.1.3.zip, r-oldrel: dfms_0.1.3.zip
macOS binaries: r-release (arm64): dfms_0.1.3.tgz, r-oldrel (arm64): dfms_0.1.3.tgz, r-release (x86_64): dfms_0.1.3.tgz, r-oldrel (x86_64): dfms_0.1.3.tgz

Linking:

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