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 |
| DOI: | 10.32614/CRAN.package.sparseDFM |
| 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 |
| Reference manual: | sparseDFM.pdf |
| Vignettes: |
Using sparseDFM - Nowcasting UK Trade in Goods (Exports) Using sparseDFM - Inflation Example |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=sparseDFM to link to this page.