Package: dfms 0.2.2

Sebastian Krantz

dfms: Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Authors:Sebastian Krantz [aut, cre], Rytis Bagdziunas [aut]

dfms_0.2.2.tar.gz
dfms_0.2.2.tar.gz(r-4.5-noble)dfms_0.2.2.tar.gz(r-4.4-noble)
dfms_0.2.2.tgz(r-4.4-emscripten)dfms_0.2.2.tgz(r-4.3-emscripten)
dfms.pdf |dfms.html
dfms/json (API)
NEWS

# Install 'dfms' in R:
install.packages('dfms', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sebkrantz/dfms/issues

Pkgdown:https://sebkrantz.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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

openblascpp

3.08 score 1 stars 12 scripts 433 downloads 10 exports 3 dependencies

Last updated 6 months agofrom:d4387f7eba. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 07 2024
R-4.5-linux-x86_64NOTEDec 07 2024

Exports:.VARainvapinvDFMem_convergedFISICrSKFSKFStsnarmimp

Dependencies:collapseRcppRcppArmadillo

Dynamic Factor Models: A Very Short Introduction

Rendered fromdynamic_factor_models.Rnwusingutils::Sweaveon Dec 07 2024.

Last update: 2023-03-31
Started: 2023-03-31

Introduction to dfms

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Dec 07 2024.

Last update: 2023-03-31
Started: 2022-10-12