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Posterior Sampling

 

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Run the posterior sampling routine selected on the posterior sampling frame on the Options tab to sample from the posterior distribution of the parameters of the DSGE model under adaptive learning. The only difference compared with the rational expectations case concerns the three extra parameters under adaptive learning, where the parameter vector will be appended with those that are to be estimated.

 

Additional Information

A details discussion of parameter transformations is found in Section 6 of the YADA Manual.
A detailed discussion about the joint Kalman filter for calculating the log-likelihood function can be found in Section 17.5 of the YADA Manual.
A detailed description of the random walk Metropolis related algorithms can be found in Section 8.1 of the YADA Manual.
A detailed description of the slice sampler can be located in Section 8.2 of the YADA Manual.
A detailed description of the Sequential Monte Carlo with likelihood or data tempering samplers are found in Section 8.4 of the YADA Manual.
A detailed description of the Importance Sampler based on the MitISEM algorithm for estimating the importance (candidate) density is provided in Section 8.5 of the YADA Manual.

 

 


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