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
Fit a Dynamic Factor Model
Generate Forecasts
Fast Stationary Kalman Filtering and Smoothing
SKF() — Stationary Kalman FilterFIS() — Fixed Interval SmootherSKFS() — Stationary Kalman Filter + Smoother
Helper Functions
.VAR() — (Fast) Barebones Vector-Autoregressionainv() — Armadillo's Inverse Functionapinv() — Armadillo's Pseudo-Inverse Functiontsnarmimp() — Remove and Impute Missing Values in a Multivariate Time Seriesem_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
Author
Maintainer: Sebastian Krantz sebastian.krantz@graduateinstitute.ch
Authors:
Rytis Bagdziunas
Other contributors:
Santtu Tikka [reviewer]
Eli Holmes [reviewer]