Run the posterior mode estimation routine for the DSGE model. The optimization routine to use can be selected in the Optimization frame on the Options tab. The maximum number of iterations and the tolerance level can also be chosen in this frame.
The optimization routines supported by YADA are: Christopher Sims' csminwel, Marco Ratto's newrat, Dynare's gmhmaxlik, and matlab's fminunc.
YADA estimates the posterior mode for the transformed parameters or for the original parameters. Since some of the parameters may have restricted support, e.g., must be positive, it is convenient to transform such parameters to a scale where the support is the real line, e.g., through the logarithmic function. From a numerical optimization perspective such a transformation means that the optimization problem no longer needs to take equality or inequality constraints into account. At the same time, the Jacobian of the transformation needs to be taken into the log posterior function that we use in the optimization.
Note that the posterior mode of the transformed parameters is an approximation of the posterior mode of the original parameters. If the data are informative about a parameter, then the posterior mode of the transformed version of this parameter is close to the posterior mode for the original version of the parameter.
This function is also available on the toolbar.
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Additional Information
• | A detailed description about the parameter transformation functions that YADA uses can be found in Section 6 of the YADA Manual. |
• | A more detailed description about posterior mode estimation can also be found in Section 7 of the YADA Manual. |
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