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sparseDFM  

Estimate Dynamic Factor Models with Sparse Loadings
View on CRAN: Click here


Download and install sparseDFM package within the R console
Install from CRAN:
install.packages("sparseDFM")

Install from Github:
library("remotes")
install_github("cran/sparseDFM")

Install by package version:
library("remotes")
install_version("sparseDFM", "1.0")



Attach the package and use:
library("sparseDFM")
Maintained by
Alex Gibberd
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-03-23
Latest Update: 2023-03-23
Description:
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'.
How to cite:
Alex Gibberd (2023). sparseDFM: Estimate Dynamic Factor Models with Sparse Loadings. R package version 1.0, https://cran.r-project.org/web/packages/sparseDFM
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