dfms

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.

Last 30 days

This package has been downloaded 386 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 16 times.

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Last 365 days

This package has been downloaded 5,592 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was May 03, 2024 with 59 downloads.

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