Dynamic Factor Models
Armadillo's Inverse Functions
Extract Factor Estimates in a Data Frame
Estimate a Dynamic Factor Model
(Fast) Barebones Vector-Autoregression
Convergence Test for EM-Algorithm
(Fast) Fixed-Interval Smoother (Kalman Smoother)
Information Criteria to Determine the Number of Factors (r)
Plot DFM
DFM Forecasts
DFM Residuals and Fitted Values
(Fast) Stationary Kalman Filter
(Fast) Stationary Kalman Filter and Smoother
DFM Summary Methods
Remove and Impute Missing Values in a Multivariate Time Series
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>.