Dynamic Factor Models
(Fast) Fixed-Interval Smoother (Kalman Smoother)
Information Criteria to Determine the Number of Factors (r)
News Decomposition
Plot DFM
DFM Forecasts
DFM Residuals and Fitted Values
Convergence Test for EM-Algorithm
Armadillo's Inverse Functions
Extract Factor Estimates in a Data Frame
Estimate a Dynamic Factor Model
Dynamic Factor Models
(Fast) Barebones Vector-Autoregression
(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 and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
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