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  "description": "Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, on datasets with missing data. The implementation follows advances in the econometric literature: estimation can be done either by running the Kalman Filter and Smoother once with initial values from PCA - following Doz, Giannone and Reichlin (2011) (2S) - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) - or using the adapted EM algorithm of Banbura and Modugno (2014), allowing estimation with 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/visualization of the model and forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002). --- Key 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. 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. 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.",
  "name": "dfms: Dynamic Factor Models",
  "codeRepository": "https://github.com/SebKrantz/dfms",
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  "license": "https://spdx.org/licenses/GPL-3.0",
  "version": "0.1.1",
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      "@type": "Person",
      "givenName": "Sebastian",
      "familyName": "Krantz",
      "email": "sebastian.krantz@graduateinstitute.ch"
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      "familyName": "Bagdziunas"
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