dfms provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). Contents:
Information Criteria to Determine the Number of Factors
ICr()
plot(<ICr>)
screeplot(<ICr>)
Fit a Dynamic Factor Model
DFM()
summary(<dfm>)
plot(<dfm>)
as.data.frame(<dfm>)
residuals(<dfm>)
fitted(<dfm>)
Generate Forecasts
predict(<dfm>)
plot(<dfm_forecast>)
as.data.frame(<dfm_forecast>)
Fast Stationary Kalman Filtering and Smoothing
SKF() --- Stationary Kalman Filter
FIS() --- Fixed Interval Smoother
SKFS() --- Stationary Kalman Filter + Smoother
Helper Functions
.VAR() --- (Fast) Barebones Vector-Autoregression
ainv() --- Armadillo's Inverse Function
apinv() --- Armadillo's Pseudo-Inverse Function
tsnarmimp() --- Remove and Impute Missing Values in a Multivariate Time Series
em_converged() --- Convergence Test for EM-Algorithm
Data
BM14_M --- Monthly Series by Banbura and Modugno (2014)
BM14_Q --- Quarterly Series by Banbura and Modugno (2014)
BM14_Models --- Series Metadata + Small/Medium/Large Model Specifications
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