Package: sparseDFM 1.0
Alex Gibberd
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) <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'.
Authors:
sparseDFM_1.0.tar.gz
sparseDFM_1.0.tar.gz(r-4.5-noble)sparseDFM_1.0.tar.gz(r-4.4-noble)
sparseDFM_1.0.tgz(r-4.4-emscripten)sparseDFM_1.0.tgz(r-4.3-emscripten)
sparseDFM.pdf |sparseDFM.html✨
sparseDFM/json (API)
| # Install 'sparseDFM' in R: |
| install.packages('sparseDFM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:e6438f1804. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 04 2024 |
| R-4.5-linux-x86_64 | OK | Nov 04 2024 |
Exports:fillNAkalmanMultivariatekalmanUnivariatelogspacemissing_data_plotraggedEdgesparseDFMtransformDatatuneFactors
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr
Using sparseDFM - Inflation Example
Rendered frominflation-example.Rmdusingknitr::rmarkdownon Nov 04 2024.Last update: 2023-03-23
Started: 2023-03-23
Using sparseDFM - Nowcasting UK Trade in Goods (Exports)
Rendered fromexports-example.Rmdusingknitr::rmarkdownon Nov 04 2024.Last update: 2023-03-23
Started: 2023-03-23
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| UK Trade in Goods (Exports) Dataset | exports |
| Interpolation of missing data | fillNA |
| UK Inflation Dataset | inflation |
| Classic Multivariate KFS Equations | kalmanMultivariate |
| Univariate filtering (sequential processing) for fast KFS | kalmanUnivariate |
| logspace | logspace |
| Plot the missing data in a data matrix/frame | missing_data_plot |
| sparseDFM Plot Outputs | plot.sparseDFM |
| Forecasting factor estimates and data series. | predict.sparseDFM print.sparseDFM_forecast |
| Generate a ragged edge structure for a data matrix | raggedEdge |
| sparseDFM Residuals and Fitted Values | fitted.sparseDFM resid.sparseDFM residuals.sparseDFM |
| Estimate a Sparse Dynamic Factor Model | sparseDFM |
| sparseDFM Summary Outputs | print.sparseDFM summary.sparseDFM |
| Transform data to make it stationary | transformData |
| Tune for the number of factors to use | tuneFactors |