Lets you view the numerical values of the log prior density, the log Jacobian and when present the log system prior density at the initial values, the DSGE posterior mode and the posterior mode values. The DSGE posterior mode is based on setting the adaptive learning parameters to their initial or calibrated values. The log Jacobian is computed as the sum of the diagonal elements of the matrix with derivatives of the original parameters, θ, with respect to the transformed parameters, ϕ. The sum of the log prior of θ and the log Jacobian is equal to the log prior of ϕ.
Additional Information
• | A more detailed description about the prior densities can also be found in Section 4 of the YADA Manual. |
• | The log Jacobian of the transformation function θ = g(ϕ) is given in Section 6. |
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