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Posterior Densities

 

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View plots of estimated marginal posterior densities for the DSGE model parameters or selected groups of the VAR parameters of a DSGE-VAR. In the kernel density estimation frame on the Miscellaneous tab you can select how you wish to estimate the posterior densities. The fast methods are (1) Gaussian, and (2) Silverman-type and Sköld-Roberts correction. The slower methods are (3) Sheather-Jones bandwidth, and (4) Bump killing bandwidth. All these methods are based on a Gaussian kernel. For details, see Sköld and Roberts (2003).

The prior densities are also displayed in the graph when they fit. In addition, YADA draws the marginal posterior mode as a solid vertical black line, and the joint posterior mode as a dashed vertical black line. In this situation, the marginal posterior concerns a single parameters, while the joint posterior mode is the joint mode based on the selected posterior mode estimator for the DSGE model parameters.

For the DSGE model parameters, it is also possible to view plots of bivariate posterior densities. Two methods are supported for such densities. The default is based on fast Fourier transforms with a Gaussian kernel and taken from the Kernel Density Estimation Toolbox of Christan Beardah. The second is based on linear diffusion processes and uses Zdravko Botev's code kde2d, which can be downloaded from Mathworks website and which is discussed in Botev, Grotowski and Kroese (2010). YADA can create both surface and contour plots of user selected pairs of bivariate densities.

 

Additional Information

A detailed description about posterior sampling of the DSGE model parameters in the DSGE-VAR model can be found in Section 15.5 of the YADA Manual.
A detailed description about posterior sampling of the VAR parameters in the DSGE-VAR model can be found in Sections 15.4 and 15.6 of the YADA Manual.

 

 


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