Historical and smoothed variables

[ming_ming]

Hi Johannes,

I am learning how to use Dynare to do Bayesian estimation with a manually linearized symmetric two-country model. I followed your paper and specified four observables and four observation equations accordingly. During my estimation process, I run into several problems.

1. With mh_replic=0, Dynare reports the "Historical and smoothed variables" for all four observables. However, only 3 of them are perfectly matched (figure attached). The last one seems to be poorly matched (the same problem arises with mh_replic>0). But there is no measurement errors specified in the model. This is the case for both version 4.4.3 and unstable version.

2. When using the unstable version, I also got the following warning:

Warning: Your prior allows for correlations between measurement errors larger than +-1 and will not
integrate to 1 due to truncation. Please change your prior
> In initial_estimation_checks at 110
In dynare_estimation_1 at 157
In dynare_estimation at 105
In example at 547
In dynare at 223

This warning did not arise in version 4.4.3. Again, I did not specify any measurement error. Anything going wrong?

3. A regular "identification" command following the reference manual cannot go through. Dynare will report an error.

4. If I instead use the following estimation command

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estimation(datafile=example_data,mh_replic=20000,mh_nblocks=5,mh_jscale=0.23244,mode_compute=0,mode_file=example_mode,mode_check,moments_varendo)

Is the following field a correct place to check the variance decomposition for an endogenous variable, say TY?

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oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.TY_hat

All related model and data files are attached. Many thanks for your time! I appreciate that.

Ming

[jpfeifer]

1. That usually means there is stochastic singularity in your model.
2. That is a bug in the warning message. Thanks for pointing it out. It should say

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correlations between structural shocks larger than +-1

This is definitely a problem when you try to do model_comparison. Otherwise, you can ignore it.
3. Identification unfortunately does not support correlations at this point. However, looking at the

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mode_check

plots, there seems to be an issue with d12 and potentially for d21
4. Yes, that is the correct field.

[ming_ming]

You are right, Johannes. I finally figured out where the stochastic singularity comes from in my model. Now the fitted variables look reasonable and I managed to obtain the global mode. Many thanks for your suggestions.