Hi all,
I have 2 little questions about Bayesian estimation on dynare:
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I am trying to estimate a large scale model (similar to QUEST III or NEMO): often, in this class of models, it is very difficult to compute the mode and the Hessian is close to be singular and cannot be inverted. My question is: using a larger set of observable variables can help dynare in computing the mode and solving the error on Hessian inversion?
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In order to avoid stochastic singularity it is required to have a number of shocks at least equal to the number of observable used to perform the estimation. What happen if I need to estimate a number of shocks larger than the observable set? Are the parameters estimated robust?
Thanks in advance for the answers and suggestions.
There are no general answers for your questions.
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It depends. If more observation provide the likelihood with a better shape, the answer might be yes. But there is no guarantee that this will help. Rather, if your data is measured with a lot of noise or you add a data series for which the model is misspecified, it might even increase your problems.
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Having more shocks than observables is no problem as long as the parameters you are estimating (including shock variances) are still identified. Concerning robustness, I would tend to say no. Only when adding shocks with true variance 0 should the estimates be unaltered. To see this, consider a data-generating process with TFP and government spending shocks. Estimating a model without allowing for government spending shocks means estimating a misspecified model. That misspecification will influence your parameter estimates. How big the problem is cannot be answered in general.
Dear jpfeifer, thanks a lot for your useful comments!!!