This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE)...

Neural networks can forecast economic data with accuracy matching that of conventional autoregressive methods such as SARIMA and VAR. This study uses dense, recurrent, convolutional, and convnet/RNN hybrids to conduct time-series analysis of interest rates, consumer and producer prices, and...