sparseDFM: Estimate Dynamic Factor Models with Sparse Loadings

Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <doi:10.48550/arXiv.2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'.

Version:	1.0
Depends:	R (≥ 3.3.0)
Imports:	Rcpp (≥ 1.0.9), Matrix, ggplot2
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, gridExtra
Published:	2023-03-23
Author:	Luke Mosley [aut], Tak-Shing Chan [aut], Alex Gibberd [aut, cre]
Maintainer:	Alex Gibberd <a.gibberd at lancaster.ac.uk>
License:	GPL (≥ 3)
NeedsCompilation:	yes
In views:	TimeSeries
CRAN checks:	sparseDFM results

Documentation:

Reference manual:	sparseDFM.pdf
Vignettes:	Using sparseDFM - Nowcasting UK Trade in Goods (Exports) Using sparseDFM - Inflation Example

Downloads:

Package source:	sparseDFM_1.0.tar.gz
Windows binaries:	r-devel: sparseDFM_1.0.zip, r-release: sparseDFM_1.0.zip, r-oldrel: sparseDFM_1.0.zip
macOS binaries:	r-release (arm64): sparseDFM_1.0.tgz, r-oldrel (arm64): sparseDFM_1.0.tgz, r-release (x86_64): sparseDFM_1.0.tgz, r-oldrel (x86_64): sparseDFM_1.0.tgz

Linking:

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