Dynamic Factor Models
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Documentation for package ‘dfms’ version 0.3.0
Help Pages
| dfms-package | Dynamic Factor Models |
| .VAR | (Fast) Barebones Vector-Autoregression |
| ainv | Armadillo's Inverse Functions |
| apinv | Armadillo's Inverse Functions |
| as.data.frame.dfm | Extract Factor Estimates in a Data Frame |
| as.data.frame.dfm_forecast | DFM Forecasts |
| BM14_M | Euro Area Macroeconomic Data from Banbura and Modugno 2014 |
| BM14_Models | Euro Area Macroeconomic Data from Banbura and Modugno 2014 |
| BM14_Q | Euro Area Macroeconomic Data from Banbura and Modugno 2014 |
| coef.dfm | DFM Summary Methods |
| DFM | Estimate a Dynamic Factor Model |
| dfms | Dynamic Factor Models |
| em_converged | Convergence Test for EM-Algorithm |
| FIS | (Fast) Fixed-Interval Smoother (Kalman Smoother) |
| fitted.dfm | DFM Residuals and Fitted Values |
| ICr | Information Criteria to Determine the Number of Factors (r) |
| logLik.dfm | DFM Summary Methods |
| plot.dfm | Plot DFM |
| plot.dfm_forecast | DFM Forecasts |
| plot.ICr | Information Criteria to Determine the Number of Factors (r) |
| predict.dfm | DFM Forecasts |
| print.dfm | DFM Summary Methods |
| print.dfm_forecast | DFM Forecasts |
| print.dfm_summary | DFM Summary Methods |
| print.ICr | Information Criteria to Determine the Number of Factors (r) |
| resid.dfm | DFM Residuals and Fitted Values |
| residuals.dfm | DFM Residuals and Fitted Values |
| screeplot.dfm | Plot DFM |
| screeplot.ICr | Information Criteria to Determine the Number of Factors (r) |
| SKF | (Fast) Stationary Kalman Filter |
| SKFS | (Fast) Stationary Kalman Filter and Smoother |
| summary.dfm | DFM Summary Methods |
| tsnarmimp | Remove and Impute Missing Values in a Multivariate Time Series |