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'.
- Version1.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release03/23/2023
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Team
Alex Gibberd
Luke Mosley
Show author detailsRolesAuthorTak-Shing Chan
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