Estimate Dynamic Factor Models with Sparse Loadings
Interpolation of missing data
Classic Multivariate KFS Equations
Univariate filtering (sequential processing) for fast KFS
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Plot the missing data in a data matrix/frame
sparseDFM Plot Outputs
Forecasting factor estimates and data series.
Generate a ragged edge structure for a data matrix
sparseDFM Residuals and Fitted Values
Estimate a Sparse Dynamic Factor Model
sparseDFM Summary Outputs
Transform data to make it stationary
Tune for the number of factors to use
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) <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'.