dfms: Dynamic Factor Models for R

The dfms R package is available on CRAN, GitHub and on the package website.

Check out the post on R-bloggers called Introducting dfms: Efficient Estimation of Dynamic FActor Models in R.

Find a first introduction in the article Introduction to dfms.

For a short theoretical overview of dynamic factor models consult the paper Dynamic Factor Models: A Very Short Introduction.

EM algorithm

The dfms package provides efficient estimation of Dynamic Factor Models via the expectation maximization (EM) algorithm, which can be performed in different ways following:

• Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. doi:10.1016/j.jeconom.2011.02.012

• Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate dynamic factor models. Review of economics and statistics, 94(4), 1014-1024. doi:10.1162/REST_a_00225

• Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133-160. doi:10.1002/jae.2306

Comparison with other R packages

The dfms R package uses C++ code, making it orders of magnitudes faster than the popular MARSS, nowcasting and nowcastDFM R packages.

Citation

Krantz S, Bagdziunas R (2023). dfms: Dynamic Factor Models. R package version 0.2.1, https://CRAN.R-project.org/package=dfms.