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-prerel: sparseDFM_1.0.zip, r-release: sparseDFM_1.0.zip, r-oldrel: sparseDFM_1.0.zip
macOS binaries: r-prerel (arm64): sparseDFM_1.0.tgz, r-release (arm64): sparseDFM_1.0.tgz, r-oldrel (arm64): sparseDFM_1.0.tgz, r-prerel (x86_64): sparseDFM_1.0.tgz, r-release (x86_64): sparseDFM_1.0.tgz

Linking:

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