Exploring the Data | Structure of the Model | Fitting a DFM | Fitting a Sparse DFM
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-4.7-arm64)sparseDFM_1.0.tar.gz(r-4.7-x86_64)sparseDFM_1.0.tar.gz(r-4.6-arm64)sparseDFM_1.0.tar.gz(r-4.6-x86_64)
sparseDFM_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
sparseDFM/json (API)
| # Install 'sparseDFM' in R: |
| install.packages('sparseDFM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Uses libs:
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:e6438f1804. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 188 | ||
| linux-devel-x86_64 | OK | 168 | ||
| source / vignettes | OK | 480 | ||
| linux-release-arm64 | OK | 212 | ||
| linux-release-x86_64 | OK | 216 | ||
| wasm-release | OK | 143 |
Exports:fillNAkalmanMultivariatekalmanUnivariatelogspacemissing_data_plotraggedEdgesparseDFMtransformDatatuneFactors
Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMatrixR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr
Using sparseDFM - Inflation Example
Last update: 2023-03-23
Started: 2023-03-23
Using sparseDFM - Nowcasting UK Trade in Goods (Exports)
Introduction | Exploring the Data | Fitting the Models | Estimated Factor Structure | Nowcasts
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 |