dfms provides efficient estimation of Dynamic Factor Models via the EM Algorithm. Estimation can be done in 3 different ways following:
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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
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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
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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
The default is em.method = "auto", which chooses "DGR" following Doz, Giannone & Reichlin (2012) if there are no missing values in the data, and "BM" following Banbura & Modugno (2014) with missing data. Using em.method = "none" generates Two-Step estimates following Doz, Giannone & Reichlin (2011). This is extremely efficient on bigger datasets. PCA and Two-Step estimates are also reported in EM-based methods.
All 3 estimation methods support missing data, with various preprocessing options, but em.method = "DGR" does not account for them in the EM iterations, and should only be used if a few values are missing at random. For all other cases em.method = "BM" or em.method = "none" is the way to go.
dfms is intended to provide a simple, numerically robust, and computationally efficient baseline implementation of (linear Gaussian) Dynamic Factor Models for R, allowing straightforward application to various contexts such as time series dimensionality reduction and multivariate forecasting. The implementation is based on efficient C++ code, making dfms orders of magnitude faster than packages such as MARSS that can be used to fit dynamic factor models, or packages like nowcasting and nowcastDFM, which fit dynamic factor models specific to mixed-frequency nowcasting applications. The latter two packages additionally support blocking of variables into different groups for which factors are to be estimated, and EM adjustments for variables at different frequencies. The package is currently not intended to fit more general forms of the state space model such as provided by MARSS.
# CRAN
install.packages("dfms")
# Development Version
install.packages('dfms', repos = c('https://sebkrantz.r-universe.dev', 'https://cloud.r-project.org'))
library(dfms)
# Fit DFM with 6 factors and 3 lags in the transition equation
mod = DFM(diff(BM14_M), r = 6, p = 3)
# 'dfm' methods
summary(mod)
plot(mod)
as.data.frame(mod)
# Forecasting 20 periods ahead
fc = predict(mod, h = 20)
# 'dfm_forecast' methods
print(fc)
plot(fc)
as.data.frame(fc)