Comparing model and data Skewness and Kurtosis moments

Hi everyone

I have a question about comparing model and data moments (Specifically in the calibration method). my question is specifically about Skewness and Kurtosis moments. I did not find any paper comparing the skewness and kurtosis of the model with the skewness and kurtosis of the data. Generally, moments such as mean, standard deviation, etc. are compared. Do you know an article that includes these moments ( Skewness and Kurtosis)?

I have run my model in dynare and the moments of the model are as described in the table below:

What I observe is that the Kurtosis of the variables in the model is very different from the Kurtosis of the variables in the data. Does it mean that my model does not explain the real world well?
I would be very grateful if you could guide me in this matter

Maybe @wmutschl knows. He should be more familiar with that literature. But it’s not very surprising. Standard models solved with a perturbation approximation are rather symmetric and feature Gaussian shocks. That leaves not much room for higher order moments to show skewness of much kurtosis.