Dynamic Factor Models

dfms provides efficient estimation of Dynamic Factor Models via the EM Algorithm — following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). Contents:

Information Criteria to Determine the Number of Factors

ICr()

• plot(<ICr>)
  

• screeplot(<ICr>)
  

Fit a Dynamic Factor Model

DFM()

• summary(<dfm>)
  

• plot(<dfm>)
  

• as.data.frame(<dfm>)
  

• residuals(<dfm>)
  

• fitted(<dfm>)

Generate Forecasts

predict(<dfm>)

• plot(<dfm_forecast>)
  

• as.data.frame(<dfm_forecast>)
  

Fast Stationary Kalman Filtering and Smoothing

SKF() — Stationary Kalman Filter
FIS() — Fixed Interval Smoother
SKFS() — Stationary Kalman Filter + Smoother

Helper Functions

.VAR() — (Fast) Barebones Vector-Autoregression
ainv() — Armadillo's Inverse Function
apinv() — Armadillo's Pseudo-Inverse Function
tsnarmimp() — Remove and Impute Missing Values in a Multivariate Time Series
em_converged() — Convergence Test for EM-Algorithm

Data

BM14_M — Monthly Series by Banbura and Modugno (2014)
BM14_Q — Quarterly Series by Banbura and Modugno (2014)
BM14_Models — Series Metadata + Small/Medium/Large Model Specifications

References

Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. doi:10.1016/j.jeconom.2011.02.012

Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate dynamic factor models. Review of Economics and Statistics, 94(4), 1014-1024. doi:10.1162/REST_a_00225

Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133-160. doi:10.1002/jae.2306