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cff-version: 1.2.0
message: 'To cite package "dfms" in publications use:'
type: software
license: GPL-3.0-only
title: 'dfms: Dynamic Factor Models'
version: 0.2.2
doi: 10.32614/CRAN.package.dfms
abstract: 'Efficient estimation of Dynamic Factor Models using the Expectation Maximization
  (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data.
  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) <https://doi.org/10.1016/j.jeconom.2011.02.012>
  - or via iterated Kalman Filtering and Smoothing until EM convergence - following
  Doz, Giannone and Reichlin (2012) <https://doi.org/10.1162/REST_a_00225> - or using
  the adapted EM algorithm of Banbura and Modugno (2014) <https://doi.org/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) <https://doi.org/10.1111/1468-0262.00273>.'
authors:
- family-names: Krantz
  given-names: Sebastian
  email: sebastian.krantz@graduateinstitute.ch
- family-names: Bagdziunas
  given-names: Rytis
repository: https://sebkrantz.r-universe.dev
repository-code: https://github.com/SebKrantz/dfms
commit: 7692ae0d78a83544fbd25f93eb0d5c923602fe99
url: https://sebkrantz.github.io/dfms/
contact:
- family-names: Krantz
  given-names: Sebastian
  email: sebastian.krantz@graduateinstitute.ch