Hi all,
For the declaration of calibrated parameters we may use the estimated_params block by specifying priors with zero variance... Or we may add dirac delta function as a new prior shape.
For the choice of the prior density I agree with Michel. The user has to choose the prior variance knowing that the weight of the prior (relative to the likelihood) will depend on the sub-sample size. Obviously we could imagine a way to automatically reweight the priors, but I don't think it would be a good idea.
Best, Stéphane.
Le mardi 26 janvier 2010 à 13:39 +0100, Michel Juillard a écrit :
Hi George,
There are however few outstanding issues to be resolved.
1.a) Is Detrend data called only once as it looks like to me now?
No. in EstimationModule, function data_filtering is called a first time and then several time in Loop on periods 1 to NP
I.e. can trend depend on different draws of parmaters and different steady states?
yes
1.b) Also, Can trend and the constant be different for different periods?
yes
- Can there be fixed (calibrated) parameters' sets for different
regimes/periods?
Good point. This should be permitted, but we don't have provision for it. Neither an interface for declararing changing calibrated parameters, nor an internal representation in the preprocessor or in Matlab. We need to think about it some more, but the resolution of this problem will only affect the design of parameter updates
- Passing SteadyState:
Do we need to return SS from estimation?
yes
If so, which one since each estimation period may eventually have its own different SS?
all of them. SS is then a matrix with as many columns as there are regimes in the model
- Should we not weight prior density for the length of the period the
parameter and its prior are valid for?
I don't think so. The prior density describes believes about the possible values of a parameter irrespective of the number of periods for which this parameter matters. If the subsample where a given paramter matters is long, the prior will have less influence on estimation as in a subsample that is short. But it is what we want.
All the best,
Michel