Combining a DSGE Model with Neural Networks for Forecasting Economic Variables

This paper examines the possibility of combining a DSGE model and neural networks to supplement each other, in order to yield a good performance in out-of-sample forecasts for economic variables. The novel neural-net structure of TDVAE (Temporal Difference Variational Auto-Encoder) proposed by Gregor et.al [2019] helps to realize this idea. TDVAE virtually replicates a stochastic state-space model through combinations of neural networks within the framework of variational Bayesian inference. Because a DSGE model provides theoretical restrictions on the state transition matrices of a state-space model, I choose to transplant those DSGE-oriented matrices into the probabilistic model of state transition in TDVAE. This TDVAE-DSGE approach certainly achieved the superior performance in the task of out-of-sample forecasts on Japan's major macroeconomic variables during 1Q/2011 and 4Q/2019