Jonathan Chassot
Jonathan Chassot
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statistical inference
Constructing Efficient Simulated Moments using Temporal Convolutional Networks
We propose a method using temporal convolutional networks for model parameter estimation by learning the mapping from sample data to its generating parameters. This map is then used for defining moment conditions in simulation-based inference. Our approach outperforms maximum likelihood estimators for small and moderate samples and effectively estimates a jump-diffusion financial model.
Jonathan Chassot
,
Michael Creel
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