bdfm-package: Bayesian and Maximum Likelihood Estimation of Dynamic Factor...
In srlanalytics/bdfm: Bayesian and Maximum Likelihood Estimation of Dynamic Factor Models
Description
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.
Details
The best way to start is to have a look at the vignette:
vignette("dfm")
See Also
dfm for the core function and more information on
package usage.
srlanalytics/bdfm documentation built on Sept. 21, 2020, 10:45 p.m.
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Description
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.
Details
The best way to start is to have a look at the vignette:
vignette("dfm")
See Also
dfm for the core function and more information on
package usage.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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