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dfms  

Dynamic Factor Models
View on CRAN: Click here


Download and install dfms package within the R console
Install from CRAN:
install.packages("dfms")

Install from Github:
library("remotes")
install_github("cran/dfms")

Install by package version:
library("remotes")
install_version("dfms", "0.2.2")



Attach the package and use:
library("dfms")
Maintained by
Sebastian Krantz
[Scholar Profile | Author Map]
First Published: 2022-10-12
Latest Update: 2023-04-03
Description:
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) - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) - or using the adapted EM algorithm of Banbura and Modugno (2014) , 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) .
How to cite:
Sebastian Krantz (2022). dfms: Dynamic Factor Models. R package version 0.2.2, https://cran.r-project.org/web/packages/dfms. Accessed 06 May. 2025.
Previous versions and publish date:
0.1.3 (2022-10-12 10:32), 0.1.4 (2023-01-12 17:00), 0.2.0 (2023-03-31 11:10), 0.2.1 (2023-04-03 13:00)
Other packages that cited dfms R package
View dfms citation profile
Other R packages that dfms depends, imports, suggests or enhances
Complete documentation for dfms
Functions, R codes and Examples using the dfms R package
Some associated functions: BM14_Models . DFM . FIS . ICr . SKF . SKFS . ainv . as.data.frame.dfm . dot-VAR . em_converged . plot.dfm . predict.dfm . residuals.dfm . summary.dfm . tsnarmimp . 
Some associated R codes: DFM.R . EMBM.R . EMBM_idio.R . EMDGR.R . RcppExports.R . data.R . init_cond.R . methods.R . my_RcppExports.R . srr-stats-standards.R . utils.R .  Full dfms package functions and examples
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