Package: dfms 0.2.2
dfms: Dynamic Factor Models
Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
Authors:
dfms_0.2.2.tar.gz
dfms_0.2.2.tar.gz(r-4.5-noble)dfms_0.2.2.tar.gz(r-4.4-noble)
dfms_0.2.2.tgz(r-4.4-emscripten)dfms_0.2.2.tgz(r-4.3-emscripten)
dfms.pdf |dfms.html✨
dfms/json (API)
NEWS
# Install 'dfms' in R: |
install.packages('dfms', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sebkrantz/dfms/issues
Pkgdown:https://sebkrantz.github.io
- BM14_M - Euro Area Macroeconomic Data from Banbura and Modugno 2014
- BM14_Models - Euro Area Macroeconomic Data from Banbura and Modugno 2014
- BM14_Q - Euro Area Macroeconomic Data from Banbura and Modugno 2014
Last updated 6 months agofrom:d4387f7eba. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 07 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 07 2024 |
Exports:.VARainvapinvDFMem_convergedFISICrSKFSKFStsnarmimp
Dependencies:collapseRcppRcppArmadillo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
(Fast) Barebones Vector-Autoregression | .VAR |
Armadillo's Inverse Functions | ainv apinv |
Extract Factor Estimates in a Data Frame | as.data.frame.dfm |
Euro Area Macroeconomic Data from Banbura and Modugno 2014 | BM14_M BM14_Models BM14_Q |
Estimate a Dynamic Factor Model | DFM |
Convergence Test for EM-Algorithm | em_converged |
(Fast) Fixed-Interval Smoother (Kalman Smoother) | FIS |
Information Criteria to Determine the Number of Factors (r) | ICr plot.ICr print.ICr screeplot.ICr |
Plot DFM | plot.dfm screeplot.dfm |
DFM Forecasts | as.data.frame.dfm_forecast plot.dfm_forecast predict.dfm print.dfm_forecast |
DFM Residuals and Fitted Values | fitted.dfm resid.dfm residuals.dfm |
(Fast) Stationary Kalman Filter | SKF |
(Fast) Stationary Kalman Filter and Smoother | SKFS |
DFM Summary Methods | print.dfm print.dfm_summary summary.dfm |
Remove and Impute Missing Values in a Multivariate Time Series | tsnarmimp |