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>.
| Version: |
0.1.4 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
Rcpp (≥ 1.0.1), collapse (≥ 1.8.0) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
xts, vars, magrittr, testthat (≥ 3.0.0), knitr, rmarkdown, covr |
| Published: |
2023-01-12 |
| Author: |
Sebastian Krantz [aut, cre],
Rytis Bagdziunas [aut] |
| Maintainer: |
Sebastian Krantz <sebastian.krantz at graduateinstitute.ch> |
| BugReports: |
https://github.com/SebKrantz/dfms/issues |
| License: |
GPL-3 |
| URL: |
https://sebkrantz.github.io/dfms/ |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
dfms results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=dfms
to link to this page.