Constructing Efficient Simulated Moments Using Temporal Convolutional Networks

Jonathan Chassot and Michael Creel

No 1412, Working Papers from Barcelona School of Economics

Abstract: We propose a method to estimate model parameters using temporal convolutional networks (TCNs). By training the TCN on simulated data, we learn the mapping from sample data to the model parameters that were used to generate this data. This mapping can then be used to define exactly identifying moment conditions for the method of simulated moments (MSM) in a purely data-driven manner, alleviating a researcher from the need to specify and select moment conditions. Using several test models, we show by example that this proposal can outperform the maximum likelihood estimator, according to several metrics, for small and moderate sample sizes, and that this result is not simply due to bias correction. To illustrate our proposed method, we apply it to estimate a jump-diffusion model for a financial series.

Keywords: temporal convolutional networks; method of simulated moments; jump-diffusion stochastic volatility (search for similar items in EconPapers)
JEL-codes: C15 C45 C53 C58 (search for similar items in EconPapers)
Date: 2023-11
New Economics Papers: this item is included in nep-ecm and nep-net
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