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YADA Version 4.40

 

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Released on 30/08/2018:

The marginal predictive moments for DSGE models can now produce back- and nowcasts provided that the model does not have any measurement errors. These predictions make sense when the observed variables have a ragged edge such that certain variables at the back- and nowcast horizon have missing values.
Variance decompositions for the state variables have been added as a complement to the conditional variance decompositions.
Added sequential estimation of the marginal likelihood for DSGE models. Model sequences can be selected and the tools run via items on the Model sequence menu-item on the File menu.
Added the option of using an external DSGE model solver.
Improved handling of error messages.

 

 


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