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This paper uses a transformer neural network model to perform imputation of missing data. The method returns a distribution which allows for easy marginalization which can allow for statistically efficient analysis when combined with a model for inference.

This paper demonstrates how to use variational inference as well as a flexible variational family that allows fast estimation of DSGE models, which roughly reduces computation time by a factor of 100 compared to Markov chain Monte Carlo.