srlanalytics/bdfm: Bayesian and Maximum Likelihood Estimation of Dynamic Factor Models
Estimates dynamic factor models by simulation using the Durbin and Koopman (2012) disturbance smoother. Maximum likelihood estimation via Watson and Engle (1983) and 2-step estimation via principal components is also supported. Input data may be mixed frequency, noisy, have missing values, or ragged edges with different start or end dates.
Getting started
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Package details |
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| Maintainer | |
| License | MIT + file LICENSE |
| Version | 0.0.2 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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