Package index • dfms

Information Criteria

Choose the number of factors and the lag-order of the factor VAR.

ICr() print(<ICr>) plot(<ICr>) screeplot(<ICr>): Information Criteria to Determine the Number of Factors (r)

Fit a Dynamic Factor Model

DFM estimation via the EM algorithm and PCA, and various methods inspect the model and extract results.

DFM(): Estimate a Dynamic Factor Model

print(<dfm>) summary(<dfm>) print(<dfm_summary>): DFM Summary Methods

plot(<dfm>) screeplot(<dfm>): Plot DFM

as.data.frame(<dfm>): Extract Factor Estimates in a Data Frame

residuals(<dfm>) fitted(<dfm>): DFM Residuals and Fitted Values

Forecasting

Forecast both the factors and the data, and methods to visualize forecasts and extract results.

predict(<dfm>) print(<dfm_forecast>) plot(<dfm_forecast>) as.data.frame(<dfm_forecast>): DFM Forecasts

Fast Stationary Kalman Filtering and Smoothing

Optimized Armadillo C++ implementations of the stationary Kalman Filter and Smoother.

SKF(): (Fast) Stationary Kalman Filter

FIS(): (Fast) Fixed-Interval Smoother (Kalman Smoother)

SKFS(): (Fast) Stationary Kalman Filter and Smoother

Helper Functions

Fast VAR, matrix inverses, imputation/removal of missing values in multivariate time series, and convergence check for EM algorithm.

.VAR(): (Fast) Barebones Vector-Autoregression

tsnarmimp(): Remove and Impute Missing Values in a Multivariate Time Series

ainv() apinv(): Armadillo's Inverse Functions

em_converged(): Convergence Test for EM-Algorithm

Data

Euro area macroeconomic data from Banbura and Modugno (2014), and 3 DFM specifications considered in their paper.

BM14_Models BM14_M BM14_Q: Euro Area Macroeconomic Data from Banbura and Modugno 2014