
Package index
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dfms-packagedfms - Dynamic Factor Models
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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.
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DFM() - Estimate a Dynamic Factor Model
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print(<dfm>)coef(<dfm>)logLik(<dfm>)summary(<dfm>)print(<dfm_summary>) - DFM Summary Methods
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plot(<dfm>)screeplot(<dfm>) - Plot DFM
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as.data.frame(<dfm>) - Extract Factor Estimates in a Data Frame
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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.
News Decomposition
Decompose forecast revisions into news contributions follwing Banbura and Modugno (2014).
Fast Stationary Kalman Filtering and Smoothing
Optimized Armadillo C++ implementations of the 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.
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.VAR() - (Fast) Barebones Vector-Autoregression
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tsnarmimp() - Remove and Impute Missing Values in a Multivariate Time Series
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ainv()apinv() - Armadillo's Inverse Functions
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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.
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BM14_ModelsBM14_MBM14_Q - Euro Area Macroeconomic Data from Banbura and Modugno 2014