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Prior - Covariance Parameters

 

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Prior type: Lets you select the marginal prior distribution for the covariance matrix of the residuals in the VAR model. You can choose between (1) a diffuse, and (2) an inverted Wishart prior.
Wishart distribution parameterization format: Provided that the prior type is an inverted Wishart, the location matrix A can be numerically parameterized through (1) the maximum likeilhood estimate of the residual covariance matrix, and (2) the identity matrix times the variance tightness hyperparameter.
Variance tightness hyperparameter: Lets you select the variance tightness hyperparameter (λA). Values between 0.05 and 100 are supported.
Degrees of freedom: Lets you select the number of degrees of freedom for the inverted Wishart prior distribution. Integer values between p+2 and p+20 are supported, where p is the number of endogenous variables in the VAR model.

 

Additional Information

A more detailed description about how to set up the Bayesian VAR prior is found in Section 14.1 of the YADA Manual.

 

 


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